*[inlinelist,1]label=(),

# Analysing Mixed Initiatives and Search Strategies
during Conversational Search

Mohammad Aliannejadi  University of Amsterdam<EMAIL_ADDRESS>,  Leif
Azzopardi  University of Strathclyde<EMAIL_ADDRESS>,  Hamed
Zamani  University of Massachusetts Amherst<EMAIL_ADDRESS>,  Evangelos
Kanoulas  University of Amsterdam<EMAIL_ADDRESS>,  Paul Thomas  Microsoft
<EMAIL_ADDRESS>and  Nick Craswell  Microsoft<EMAIL_ADDRESS>
(2021)

###### Abstract.

Information seeking conversations between users and Conversational Search
Agents (CSAs) consist of multiple turns of interaction. While users initiate a
search session, ideally a CSA should sometimes take the lead in the
conversation by obtaining feedback from the user by offering query suggestions
or asking for query clarifications i.e. mixed initiative. This creates the
potential for more engaging conversational searches, but substantially
increases the complexity of modelling and evaluating such scenarios due to the
large interaction space coupled with the trade-offs between the costs and
benefits of the different interactions. In this paper, we present a model for
conversational search – from which we instantiate different observed
conversational search strategies, where the agent elicits: (i) Feedback-First,
or (ii) Feedback-After. Using 49 TREC WebTrack Topics, we performed an
analysis comparing how well these different strategies combine with different
mixed initiative approaches: (i) Query Suggestions vs. (ii) Query
Clarifications. Our analysis reveals that there is no superior or dominant
combination, instead it shows that query clarifications are better when asked
first, while query suggestions are better when asked after presenting results.
We also show that the best strategy and approach depends on the trade-offs
between the relative costs between querying and giving feedback, the
performance of the initial query, the number of assessments per query, and the
total amount of gain required. While this work highlights the complexities and
challenges involved in analyzing CSAs, it provides the foundations for
evaluating conversational strategies and conversational search agents in
batch/offline settings.

Conversational Search, Mixed Initiatives, Evaluation

††journalyear: 2021††copyright: acmcopyright††conference: Proceedings of the
30th ACM International Conference on Information and Knowledge Management;
November 1–5, 2021; Virtual Event, QLD, Australia††booktitle: Proceedings of
the 30th ACM International Conference on Information and Knowledge Management
(CIKM ’21), November 1–5, 2021, Virtual Event, QLD, Australia††price:
15.00††doi: 10.1145/3459637.3482231††isbn: 978-1-4503-8446-9/21/11††ccs:
Information systems Users and interactive retrieval††ccs: Human-centered
computing HCI design and evaluation methods

##  1\. Introduction

Conversational Search (CS) is an emerging area of research that aims to couch
the information seeking process within a conversational format (Anand et al.,
2019; Culpepper et al., 2018; Croft, 2019). CS differs from the traditional
query-response paradigm by providing more agency through improved query
understanding and the persistence of the conversational context (Yan et al.,
2016; Vtyurina et al., 2017). The exciting prospect of Conversational Search
Agents (CSAs) has spurred considerable research into the development of the
underlying methods to support such agents. Of particular interest have been
methods that facilitate mixed initiative approaches that aim to enhance the
agent’s understanding of the user’s information need through query suggestions
(i.e. refinements, expansions, etc.) or through query clarifications (i.e.
questions that seek to clarify the query, elicit the user preferences, etc.)
(Rao and Daumé, 2018; Zamani et al., 2020a; Aliannejadi et al., 2019; Krasakis
et al., 2020; Aliannejadi et al., 2020, 2021). This is because mixed
initiative interactions are seen as a key property of a conversational search
agent (Radlinski and Craswell, 2017) which has the potential to increase user
engagement and user satisfaction (Kiesel et al., 2018). While various efforts
have focused on building the infrastructure to support the inclusion of
clarifying questions, and numerous methods proposed to generate or select good
questions (Braslavski et al., 2017; Aliannejadi et al., 2019; Zamani et al.,
2020a, b; Hashemi et al., 2020; Sekulic et al., 2021; Lotze et al., 2021),
little work has evaluated or compared the use of such methods within the
context of a CS session in a batch/offline setting — largely because the
possible state space increases exponentially with interaction, coupled with
the lack of a user model for CS. So while there has been considerable effort
in the community to engage in single and mixed initiative conversations,
little has been done to understand how they impact performance during CS
sessions.

In this work, our goal is to provide a user model for conversational search
that can be used to evaluate mixed initiative approaches and conversational
strategies. While asking query clarifications and offering query suggestions
may lead to increases in user satisfaction in certain scenarios (Kiesel et
al., 2018), it also imposes additional costs on the user; the premise being
that the investment in feedback will lead to greater returns later. And so the
costs and the expected gains associated with different mixed initiative
approaches will determine whether eliciting or giving feedback is worthwhile
compared to other actions that could be taken (e.g. re-querying or assessing)
(Azzopardi et al., 2018a; Trippas et al., 2018a). So how should an agent
interact with a user? Should it ask a series of clarifications, and then
present results, or present results, and then ask for clarifications? Or, not
ask any clarifications? It is very much an open question what conversational
search strategy should be employed in order to minimise the conversational
cost while maximising the user’s gain. And, how different mixed initiative
approaches would influence the choice of strategy given the user’s
interactions (i.e. whether they assess more, give more feedback, or issue more
queries). In this paper, we aim to provide insights into these research
questions by modelling the CS process and then measuring the costs and
benefits of different CS strategies and mixed initiative approaches.

##  2\. Background

Over the past few years, an increasing amount of attention has been directed
toward developing methods that enable CS and the development of CSA, for
example: ranking results given the conversation (Yang et al., 2018; Dalton et
al., 2019), generating clarifying questions (Aliannejadi et al., 2019; Zamani
et al., 2020a, c), studying system-initiative interactions (Wadhwa and Zamani,
2021), and presenting results (Spina et al., 2017). Less attention, however,
has been focused on developing user models for evaluating CS which can be used
to analyse CSAs and CS strategies.

One of the first CS systems was proposed by Croft and Thompson (1987), called
I3R. It acted as an expert intermediary system, communicating with the user
during a search session. Since then, other researchers have developed more
elaborate approaches. For example, Belkin et al. (1995) offered users choices
in a search session using case-based reasoning. While, Allen et al. (1999)
were among the first to study mixed initiative conversations, which they
defined as “a flexible interaction strategy in which each agent (human or
computer) contributes what it is best suited at the most appropriate time“.
However, since then researchers have mainly focused on single-initiative
interaction such as rule-based conversational systems (Walker et al., 2001)
and spoken language understanding approaches (Pieraccini et al., 1992; He and
Young, 2005). Mixed initiative, though, provides a mechanism for the agent to
improve its understanding of the user’s information need by obtaining feedback
by offering query suggestions (i.e. refinements to the query) or query
clarifications (i.e. questions that seek to clarify the query) (Rao and Daumé,
2018; Aliannejadi et al., 2019; Zamani et al., 2020a). As previously
mentioned, this idea of mixed initiative and the system taking agency has led
to the development of CSAs. Inspired by models and work on conversations and
dialogue systems (e.g. COR, etc. (Belkin et al., 1995; McTear, 2002; Oddy,
1977; Sitter and Stein, 1992)), Radlinski and Craswell (2017) developed a
theoretical framework, that puts forward five key properties that a search
system needs to have in order to be “conversational”. These properties are:

  * •

User Revealment where the user discloses to the agent their information needs,

  * •

Agent Revealment where the agent reveals what the agent understands, what
actions it can perform, and what options are available to the user,

  * •

Set Retrieval where the agent needs to be able to work with, manipulate and
explain the sets of options/objects which are retrieved given the
conversational context,

  * •

Memory where the agent tracks and manages the state of the conversation and
the user’s information need, and,

  * •

Mixed Initiative where both the agent and the user can take the initiative and
direct the conversation search process.

Azzopardi et al. (2018a) extended this framework by defining the specific
actions associated with these aspects. For example, within mixed initiative,
they suggest that agents could seek to provide query suggestions or query
clarifications that help to refine the user’s information need, or seek to
elicit the user’s preferences, while users could, conversely, suggest
refinements and disclose preferences. Trippas et al. (2018a) examined how
searchers interacted with intermediaries (who used the search engine), engaged
actions and observed that searchers generally switched between query
formulation (user revealment) and result exploration (set retrieval), but also
provided relevance feedback and clarifications. Following on from this work,
Trippas et al. (2020) suggested a more general classification of the different
interactions grounded by empirical studies – their high level model delineates
between: (i) discourse level actions, that enable discourse management,
grounding, visibility, and navigation and (ii) task level actions, that are
specific to the search such as handling queries, search assistance (e.g.
clarifying queries), presenting results, and search progression. In another
empirical study that analysed a number of CS datasets, Vakulenko et al. (2019)
found that users issue a query to the agent, and then the agent may respond
with a request to clarify/refine the information need, or provide a list of
results. The user could then respond by either issuing a new query, responding
to the request, providing feedback, or assessing a result. They referred to
this as the QFRA model (Vakulenko et al., 2019). They observed different
patterns of behaviour such as query-feedback loops, where several rounds of
feedback to clarify/refine their query were observed, before results were
assessed (Feedback-First), and assessment-feedback loops, where the user
inspected results and then provided feedback to clarify/refine their query
(Feedback-After).

Zhang et al. (2018) proposed a System Ask, User Respond paradigm, which is
akin to query-refinement, where after the initial request is made, the agent
will ask for refinements/clarifications until it is confident enough to
present results. Kaushik et al. ([n. d.]) presented a system sided workflow
model consisting of the steps the agent takes when dealing with a user’s
request (e.g. handling greetings and error handling). Under their model when a
query is entered, it is checked and if a clarification is needed, the user is
asked for one clarification, else, the agent retrieves and presents three
results. If these are not relevant, or the user wants to see more items, they
can request another three results. Alternatively, they can request to view the
document (or a summary of). Otherwise, they can issue a new query (or stop). A
similar approach is presented in (Wambua et al., 2018) where up to eight
rounds of feedback were performed. While Dubiel et al. (2020) presented a
similar conversational workflow model, however, the agent asks for up to three
rounds of feedback to refine the user’s request, before presenting two
results. More recently, Lipani et al. (2021) they proposed a CS model based on
exploring subtopics via a query-response paradigm, however, it did not
consider feedback. In this work, we aim to explicitly model feedback and
explore its impact within the conversational search process.

The various models proposed share a number of commonalities inherent to
Interactive Information Retrieval (IIR). Interaction consists of a number of
turns based around: Query Formulation, where the user expresses their query,
Result Exploration, where the user examines results, and Query Reformulation,
where the user updates their query (Marchionini, 1997; Sahib et al., 2012). In
terms of modelling, simulating, and evaluating the IIR process, the focus has
largely been on considering sessions, rather than mixed initiatives. For
example, the user browsing model which is at the heart of most IR evaluation
metrics, assumes a user will pose a query, and then examine documents in a top
down fashion (Carterette, 2011; Moffat and Zobel, 2008). For IIR, the model
has been extended such that the user decides with some probability of
examining the next document, or issuing a new query (Baskaya et al., 2012), or
examining a fixed number of documents before issuing a new query (Azzopardi,
2011). Through modelling the IIR process it has been shown trade-offs emerge
between querying and assessing (Azzopardi, 2011), where, for example,
Azzopardi et al. (2013) found that as query cost increased, users submitted
fewer queries, and compensated by examining more results. But, to model CS, it
is clear that the mixed initiative (feedback turn) needs to be explicitly
considered within the user model. However, this will add additional
complexities – and invariably introduce new trade-offs because gathering
feedback to refine or clarify the query will come at a cost – which may or may
not lead to more gain – and so issuing a new query or assessing another result
may be more beneficial. In (Azzopardi et al., 2018a; Trippas et al., 2020),
they point out that it is important for CSA to maximise the gain it delivers
to the user, while trying to minimise the cost – and thus maximise the rate of
gain (following Grice’s Maxims of Conversation (Grice, 1975)). With the
introduction of mixed initiative approaches and different CS strategies for
engaging with a CSA there are many open questions. Is giving feedback
(clarifications or suggestions) worth the cost, under what conditions is it
beneficial, and, what type of CS strategy (feedback first or after) leads to a
higher rate of gain?

Figure 1. Example chat-based CSA where one agent asks clarifications questions
before presenting results (top), while the other agent asks query suggestions
after presenting results (bottom). An open question is what strategy (e.g.
initiate first or after results) and what type of mixed initiative (e.g.
clarifications or refinements) leads to a better CS?

##  3\. A Model of Conversational Search

As previously discussed, conceptually conversational search can be seen as a
special case of IIR (Croft, 2019) – where the interaction between the user and
the agent is based around conversational turns – and the agent is more active
in the seeking process through mixed initiative interactions. So far we have
not specified the details of the CSA, which could be: (i) a voice only CSA
often via a virtual assistant (Kiseleva et al., 2016; Trippas et al., 2020;
Dubiel et al., 2020; Trippas et al., 2018b; Sahib et al., 2012)), (ii) a chat
based CSA (that is in-situ within a platform like Slack (Avula, 2017) or
Telegram (Zamani and Craswell, 2020)), (iii) an augmented search engine
interface (Bosetti et al., 2017), or (iv) a multi-modal virtual assistant
(Zamani and Craswell, 2020; Johnston et al., 2014). Given the wide range of
CSAs, it is not possible to fully model them all – so we need to make a number
of assumptions about the type CSA we will model. Below, we outline what
affordances the agent provides, and then what actions a user can take with
respect to the search process e.g. we are focusing on the salient search
interactions (task level), and not on modelling error handling, chit-chat,
etc.(discourse level). Given a CSA, we assume that the agent can initiate or
respond as follows:

  * (i)

present results/answers to the user given their query,

  * (ii)

request additional feedback to update the query,

  * (iii)

or some combination of these.

The response (or combined response, and order of) will depend on numerous
factors e.g. the modality of the agent, bandwidth available, and agent
capabilities. We assume that the user in turn can perform the following
actions during the search process:

  * (i)

issue a query,

  * (ii)

provide feedback, or

  * (iii)

assess a result/answer.

Given the affordances of the agent and how the user can interact with the
agent, then we can conceptualise the conversational search process as a series
of turns consisting of three main turn types: ($\tau_{Q}$) query turn,
($\tau_{F}$) feedback turn, and ($\tau_{A}$) assessment turn.

As previously mentioned, how the agent responds will be different depending on
the type of agent, its modality, and the current context. For example, (i) a
voice only CSA needs to be very sensitive to the limited and serial bandwidth
of speech, and so responses are likely to be shorter, (ii) a multi-modal CSA
could present a more detailed combined response i.e. a search engine result
page that asks a number of requests for feedback (via query suggestions,
facets, etc.), provides many results, etc., while (iii) a chat based CSA agent
has some restrictions on screen space and bandwidth within the chat window,
but has the advantage over voice only CSAs because the conversation is
persistent so the user can refer back to options, etc.. For the purposes of
this work, we will assume that the CSA is a chat-based CSA like that in Fig. 1
which represents the interfaces explored in (Avula, 2017; Zamani and Craswell,
2020; Kaushik et al., [n. d.]). Of note is that depending on the interface and
its modality, the cost of different conversational turns will vary – and this
will impact on how much gain the user accumulates from their conversation with
the agent. As proposed in (Azzopardi et al., 2018a; Trippas et al., 2018b), we
also assume that a user wants to maximise the amount of gain they receive from
the system, while trying to minimise the cost of the conversation (where the
cost could be the total number of turns, or the total time taken to perform
those turns). Note that the assumed objective does not necessarily mean that a
user prefers a shorter conversation, but one that yields a higher rate of
gain. An open question then is how should a user and CSA work together in
order to maximise the user’s rate of gain? Should the user give many rounds of
feedback, or should they examine some results, and then give feedback to the
system? Should the CSA request more feedback, or provide more results?

###  3.1. Modelling the Conversation Search Process

To model the conversation search process, we draw upon the previous work that
has conceptualised conversational and interactive search, with the aim to
formalise the key actions/turns described above within a Markov Decision
Process (MDP) model (as done in (Baskaya et al., 2012; Maxwell and Azzopardi,
2016; Moffat and Zobel, 2008; Azzopardi et al., 2019; Thomas et al., 2014) for
IIR). In these works, MDPs (or variants of) have been used to represent key
decisions that users make when searching and interacting with a system and
their key actions that they take. For example, the simple User Browsing Model
(UBM) that underpins most metrics in IR (Carterette, 2011), assumes that a
user will issue a query, then assess a result item accumulating some gain if
it is relevant. The user then decides to either continue and assess the next
item with some probability of continuing or stop examining result items
(Moffat and Zobel, 2008; Moffat et al., 2013). In (Thomas et al., 2014;
Maxwell and Azzopardi, 2016), the UBM was extended to IIR to include
additional decision points to model session search. However, because
conversational search affords mixed initiative where feedback can be elicited
from the user, the process is much more complicated. This additional
affordance means that we need to include other decision points to capture
these conversational turns – and integrate the feedback process with the
browsing process within the larger context of the search session.

Fig. 2 presents our model of the CS process – where we have broken up each of
the user choices into binary decisions (denoted by diamonds), and the three
actions/turns with circles. We assume that the user starts the search process
by issuing a query ($\tau_{Q}$). Given the query, the agent responds either
with a list of results, clarifications, suggestions, or a combination of.
Essentially the agent presents the “search engine result page” either via a
web page or a chat bot via text (as in the Figure 1) or through speech. Given
the response, the user may decide to either inspect results or give feedback
depending on what options are presented by the agent. If they choose to assess
a result ($\tau_{A}$) they follow the typical UBM, shown in light purple.
While if they choose to provide feedback ($\tau_{F}$) then they follow the
User Feedback Model (UFM) shown in light green.

Following the UBM, if a user performs a $\tau_{A}$ turn, then they inspect a
result item, where it is assumed that they will accumulate some gain if the
result is relevant, and then they need to decide whether to perform another
$\tau_{A}$ turn, or not (Moffat and Zobel, 2008; Moffat et al., 2013). The
decision to continue, would of course, depend on a number of factors such as
how much gain has been accumulated, how many items have been
examined/assessed, etc. (Moffat et al., 2013). Once they decide to stop
assessing, the user may decide to give feedback to the agent, in order to
refine/expand their current query, or not. If not, the user can then decide
whether to re-formulate their query, in which case repeating the process.
Otherwise, they stop searching. Similarly, in the case, where the user decides
to give feedback ($\tau_{F}$), the can provide feedback to the agent, where it
is assumed that the agent will provide an updated response, and then the user
needs to decide whether to perform another round of feedback ($\tau_{F}$), or
not. Once they stop giving feedback, the user can decide whether to go back to
assessing, or not. And, if not they then need to decide whether to re-query,
or stop searching altogether.

Figure 2. The User Model of Conversational Search which is composed of three
sub-components the Querying, Browsing and Feedback Models. Diamonds represent
user decision points, while circles represent the action/turn taken.

Fig. 1 presents two example conversations. In the top example, the user issues
a query, and the agent responds by asking a query clarification, the user
responds and then the agent presents a number of results. In the bottom
example, the user issues a query, and the agent responds with a number of
results, followed by a request for feedback via query suggestions, to which
the user responds, and the conversational continues. However, the space of
possible sequences of different turns grows rapidly. And, herein lies the
complexity of evaluating conversational search – after $t$ turns, the number
of possible conversational sequences, is approximately $3^{t}$ for a fully
mixed initiative CSA. Nonetheless, the number of possible sequences of
conversational turns exponentially increases with the number of turns. This
presents an open challenge in evaluating Conversational Search Agents.

###  3.2. Instantiating the User Model for CS

In order to make the problem tractable, we need to reduce the number of
possibilities so that we can simulate and then evaluate the CS process.
Grounded by observed behaviours from (Vakulenko et al., 2019), we propose two
strategies for conversational search:

  * •

Feedback First (FF): where the user performs a query-feedback loop before
assessing. That is, after querying the user given $F$ rounds of feedback,
before assessing $A$ items.

  * •

Feedback After (FA): where the user performs assessment-feedback loops, where
after assessing $A$ items, the user gives feedback, and then repeats the
process $F$ times.

These two interaction models represent two “pure” strategies that users/agents
might evolve/apply. The first approach, Feedback-First, represents a CSA that
is like a Librarian or Booking agent.Here, the agent asks the user a number of
clarifying questions or makes a number of suggestions to refine the user’s
information need before presenting results to the user. The second approach,
of Feedback After, represents a more exploratory search setting where the user
learns about the topic, and then provides feedback to the agent to progress
their search through the topic space. While in practice it is likely that the
optimal CS strategy would be a mixture of FA and FF, investigating these
strategies is feasible, and has not been previously evaluated. Given these two
strategies, we aim to draw insights into how and when they are more successful
and under what conditions. For example, how do the performance of the initial
query, the cost of turns, the type of feedback, and the searcher’s strategy
interact and influence performance?

###  3.3. Evaluating the Gain and Cost of CS

While evaluation in a traditional IR setting is primarily concerned with
measuring the expected utility of a ranked list, CS introduces an interaction
space that grows exponentially with interaction. This makes evaluating
different strategies and methods more complicated, because the different
interactions have different costs and provide different benefits. For example,
giving feedback comes at a cost, on the hope that it will lead to accruing
more gain later on. Obviously, if feedback turns are expensive, and they don’t
lead to greater increased gain, then the “conversational” part of the search
may not be beneficial. To represent the costs associated with each action, we
model the cost for the conversational turns $\tau_{Q}$, $\tau_{F}$ and
$\tau_{A}$ as $c(\tau_{Q}$), $c(\tau_{F}$) and $c(\tau_{A})$, respectively.
The cost associated with each turn will depend on the response and the
modality of the CSA. Consequently, when considering which CS strategy or which
CSA is better than another, we cannot be agnostic to the cost of the
conversation. Both cost and gain arising from the CS need to be measured. In
the CS setting, we can generalise the cumulative gain metric from traditional
IR evaluations (Järvelin and Kekäläinen, 2017) to turns, where the Turn-based
Cumulative Gain of a sequence of conversational turns is:
$G(t_{1},\dots,t_{T})=\sum_{i=1}^{i=T}g(t_{i})$ where $T$ is the total number
of turns, and $g(t_{i})$ is the gain obtained from the $i$th turn, and $t_{i}$
is either $\tau_{Q}$, $\tau_{F}$ or $\tau_{A}$. As each turn comes at a cost,
then the total cost, is: $C(t_{1},\dots,t_{T})=\sum_{i=1}^{i=T}c(t_{i})$,
where $c(t_{i})$ is the cost of performing the $i$th turn. The subsequent rate
of gain, can then be calculated as total gain divided by the total cost:
$R(t_{1},\dots,t_{T})=\frac{G(t_{1},\dots,t_{T})}{C(t_{1},\dots,t_{T})}$ While
discounting or session based metrics could be applied (Järvelin et al., 2008;
Azzopardi et al., 2018b; Lipani et al., 2021), we leave such directions for
further work, as it is not clear how the discounts would or should be applied
in this context.

##  4\. Research Questions

In the context of conversational information seeking, where a user wants to
explore a topic, and find out about various facets of the topic, through a
conversational chat bot interface (like the one in Fig. 1), we aim to obtain
insights into the following research questions:

  * •

How does the conversational strategy (Feedback-First or Feedback-After) affect
performance?

  * •

How does the mixed initiative approach (Clarification or Suggestion) affect
the performance?

  * •

How is the strategy and/or approach affected by the quality of the initial
query?

  * •

How is the strategy and/or approach affected by changes in the cost of turns?

##  5\. Experimental Method

To answer our research questions, we have opted to undertake a simulated
analysis as done in previous works on IIR (Jordan et al., 2006; Azzopardi et
al., 2007; Keskustalo et al., 2008; Huurnink et al., 2010; Maxwell et al.,
2015; Maxwell and Azzopardi, 2016; Zhang and Balog, 2020). This is because the
space of possible interaction sequences is very large and evaluating the
different combinations would not be feasible in a user study. However, we do
ground our analysis by conducting a user study to obtain estimates of the
costs of performing different turns using a text based CSA (as in Fig. 1).

Collection. Following Aliannejadi et al. (2019) we use the topics created as
part of the TREC Web Track from 2009 to 2012, based on the ClueWeb09-Category
B collection. The collection consists of 198 topics. Each topic consists of a
series of facets that the user would like to explore – making them suitable to
explore in a conversational manner because clarifications and suggestions can
help refine or redirect the search towards the different facets that the user
wants to explore. To ensure having a reasonable space of exploration, we
filter out the topics that have fewer than four facets or fewer than ten
relevant documents. These steps lead us to 49 topics with a total of 211
facets (approx 4.3 per topic).

Conversational Search Agent. For our study, the CSA is defined by: (i) the
conversational strategy that it employs either Feedback-First (FF) or
Feedback-After (FA), (ii) the mixed initiative approach of Query Clarification
(QC) or Query Suggestion (QS), (iii) the number of rounds of feedback that it
offers (F), and (iv) the number of result items it presents to be assessed by
the user (A).

Retrieval of Results. Given the query, and any subsequent clarifications or
suggestions, we pre-process the query terms (i.e. stopword removal and
stemming) and submit it to the retrieval system. To retrieve the ranked list
of documents, we use an extension of the Query Likelihood Model (QLM) for CS
proposed in Aliannejadi et al. (2019) with the suggested parameters. The model
is a linear interpolation of the language model based on the query submitted
by the user, and the language model based on the feedback. Once the results
are retrieved, the result lists are filtered, and only previously unseen
result items are presented to the user. We assume the CSA has a memory of what
results the user has already seen.

User Interactions. Following the user model presented in Fig. 2, we assume
that the user follows the search strategy given by the specific CSA. Below we
describe how our simulated users generate queries which they issue during
query turns, and then describe the feedback presented to them during feedback
turns.

Query Generation (Q). To generate the queries we employed the approach given
by (Jordan et al., 2006; Azzopardi et al., 2007). For each topic, a language
model is created given the set of documents relevant to the topic. Then, to
generate a query of length $L$, terms are sampled without replacement from the
top 20 terms given their relative entropy in the language model.

Feedback (F) - Query Clarifications and Query Suggestions. Given the query
issued, we assume that the agent is able to either (i) ask clarifying
questions or (ii) provide query suggestions. The answers to the clarifying
questions, or selection of the query suggestions, are then used to improve the
query representation. Each ${\tau_{F}}$ turn is expected to lead to a better
query representation, which in turn should lead to improved query performance.
We take two approaches for simulating feedback:

  * •

Query Clarifications. For clarifications, we used the query clarifications
from the Qulac dataset (Aliannejadi et al., 2019) along with the human
responses. We followed (Krasakis et al., 2020) and pre-processed the data to
remove redundant clarifications and low quality answers.

  * •

Query Suggestions. For suggestions, we used the same query generation
algorithm as before to generate additional terms used as suggestions. As shown
in Fig. 1, the user is presented with four query suggestions, and when giving
feedback the user selects a suggestion at random.

Each successive round of feedback given, adds additional terms to the original
query. We checked the performance of the resulting expanded queries given the
query clarifications or suggestions and found that there was no significant
difference between the two approaches at neither P@10 nor P@20.

Calculating the Gain. To calculate the gain, we follow Section 3.3, where we
assume that the user only accumulates gain on an assessment turn ($\tau_{A}$),
where $g(\tau_{A})=1$ when the user assesses a previously unseen relevant
item, otherwise $g(\tau_{A})=0$ and for the other turns:
$g(\tau_{Q})=g(\tau_{F})=0$. That is, a user only received gain when they are
provided with relevant and novel information during the conversation (as done
in (Azzopardi, 2011; Baskaya et al., 2012; Smucker and Clarke, 2012; Maxwell
and Azzopardi, 2016)).

Estimating the Cost. To ground the estimation of the costs for each of the
conversational turns, we conducted a user study where we designed four
crowdsourcing tasks (HITs) on Amazon Mechanical Turk111http://mturk.com. In
all of our tasks, we first showed the user a search topic description (from a
total of five search topics from the TREC Web Track). Our choice of topic was
based on their difficulty and type (informational and faceted), aiming to
cover a wide spectrum of search tasks in the study. Each search session
started with a query from the user. Once the user clicks the Search button,
they were shown either a result snippet or document and asked to judge its
relevance (definitely relevant, possibly relevant, non-relevant). As soon as
the worker assessed one snippet (or document), we show the next snippet (or
document). We repeated this process for five results. After assessing the
fifth result, we instructed the workers to either: (i) reformulate their query
to look for a different facet of the topic, (ii) provide feedback by answering
a clarifying question, or (iii) select one of the four query suggestions. Each
HIT provided data for 20 result assessments, 1 or 4 queries, and 3 rounds of
feedback. We had 81 workers undertake the HITs, who submitted 144 queries,
assessed 1,280 result snippets, 1000 result web pages, and provided 268
responses to feedback. The average time taken to issue a query was $29.3$
seconds, to assess a result snippet was $6.3$ seconds, to assess a result web
page was $17.0$ seconds, while the average time to provide feedback was $8.3$
seconds. While in practice the cost of selecting suggestions vs. providing
clarifications will differ depending on the implementation we wanted to
compare the two approaches as fairly as possible – and thus kept the feedback
costs the same between mixed initiatives.

To calculate the total cost, we follow Section 3.3 where we set the costs as:
$c(\tau_{Q})=29.3$, and $c(\tau_{F})=8.3$. For estimating $c(\tau_{A})$ we
draw upon past work (Smucker and Clarke, 2012; Baskaya et al., 2012), where
the cost of assessing an item depends on its relevance, such that:
$c(\tau_{A})=c(s)+c(d)\big{(}P(C=1|R=1)+P(C=1|R=0)\big{)}$ where the cost of
inspecting a snippet is $c(s)=6.3$, the cost of inspecting the document is
$c(d)=17.0$ and $P(C=1|R)$ is the probability of clicking on the item given
its relevance. In this work, we set $P(C=1|R=1)=1$ and $P(C=1|R=0)=0$, and
thus assume the user only inspects relevant items but always pays the cost of
examining the snippet regardless of relevance. One could explore more
sophisticated click models, e.g., to account for position and trust bias. We
leave exploration of these options for future work. While this is a very
optimistic setting – we found including mis-clicks on non-relevant items or
lower probabilities of clicking relevant items had little impact on which
strategy/approach resulted in a higher rate of gain – only that changes
lowered the overall rate of gain of all conditions. Instead, our findings show
that the relative costs between querying and giving feedback play a much
larger role in the choice of strategy/approach (see §6.4). We leave modelling
other variations in cost for future work.

Simulated Analysis. To perform the analysis, we first decided on the CS
strategy (i.e. FF or FA) and a mixed initiative approach (i.e. QC or QS) the
agent would adopt, and then simulated the interaction as follows. For each
topic, we assume that a user submits a query to the agent. The user either
gives feedback and then examines results, or examines results and then gives
feedback depending on the CS strategy. We recorded costs and gains as the
number of queries (Q) is varied from 1 to 15, the number of rounds of feedback
(F) is varied from 1 to 10, and the number of results assessed (A) is varied
from 1 to 20. The total number of conversational turns for the FF strategy is
$Q\times(F+A)$ and for FA strategy is $Q\times(F+1)\times A$. The entire
process was repeated 20 times for each of the 49 topics, for each strategy and
mixed initiative (2x2). To explore the influence of query quality on CS we
varied the length of queries during the generation process from 1 to 4. This
resulted in over 12 million simulated CS sessions being generated for our
analysis222Code and data: https://github.com/i2lab/cikm21-conversational-
search-strategies..

##  6\. Results and Analysis

To focus the presentation of our results, we will constrain our reports to the
interactions within ten minutes (of simulated time) – and unless stated
otherwise, present the results when the starting query is of length two (L=2).

###  6.1. Conversational Trade-offs

To provide some insights into the trade-off between the different
conversational turns, in Fig. 3 we have plotted queries (Q) vs assessments (A)
for the different rounds of feedback (F) for each conversational strategy and
mixed initiative approach. The plots show the interactions for approx. 10
minutes of simulated time. From the plots we can see that as more rounds of
feedback are included, the number of queries and the number of assessments
decrease – because given a conversational search session of a similar length,
taking an F turn comes at the expense of taking an alternative turn. When we
compare the Feedback-First strategy (left) to a Feedback-After strategy
(right), we can see that Q and A decrease by a greater amount. Recall, though,
the subtle difference between conditions: in the FF strategy users perform F
rounds of feedback, then examine A items, while in the FA strategy every round
of feedback means they examine A items. If we examine the query plots, we can
see that as the number of rounds of feedback (F) increases, then the number of
queries issued (Q) and the number of assessments performed (A) decreases. For
the FA strategy the number of possible queries decreases at a much faster rate
as F increases because of the successive rounds of assessing after each round
of feedback. Given the space of possible conversational sequences, we now turn
our attention to comparing how well the different combinations of strategy and
initiative perform. To make our comparisons we will be reporting the rate of
gain, because different combinations lead to different session lengths,
depending on the conversation turns taken and the relevant items found – also
reporting the rate of gain also means we can visualize the performance w.r.t
the different number of interactions.

(a) FF-QC (b) FA-QC (c) FF-QS (d) FA-QS (e)

Figure 3. The plots show the trade-off between querying (Q) and assessing (A)
for different levels of feedback (F) for the two strategies and two mixed
initiatives. Left: Feedback First and Right: Feedback After. Top: Query
Clarification and Bot.: Query Suggestion. For clarity, only F$\leq$5 and
Q$\leq$10 is shown.

###  6.2. Conversational Strategy vs. Mixed Initiative

To answer our main research question of how performance is affected by the
conversational strategies: Feedback-First (FF) or Feedback-After (FA), vs. the
Mixed Initiative (MI) approaches: Query Clarification (QC) or Query Suggestion
(QS), we considered how the rate of gain (R) changed as the number of
assessments (A) and levels of feedback (F) were varied for different CSA
combinations.

First, we can how the different MI approaches perform for the FF strategy by
inspected the left hand plots in Fig. 4. The top left plot shows that when
query clarifications are employed it leads to substantial increases in the
rate of gain over the baseline (i.e. when no feedback is given/provided
$F=0$). Additional rounds of feedback increase the rate of gain but with
diminishing returns. While as the number of assessments per query (A)
increases, the rate of gain also increases. This makes sense, because the
investment in improving the query means that more relevant information is
surfaced later on. However, when query suggestions are offered with the FF
strategy, we observed a similar trend in the bottom left plot, but not as
pronounced. In fact, after two rounds of query suggestions the rate of gain
starts to decrease such that five iterations results in similar gain to the no
feedback baseline.

Table 1. For each combination of CSS x MI and for the no feedback condition (F$=0$), the best settings (query Q∗, assessment A∗, feedback F∗, cost C∗, and rate of gain R∗), on average, to achieve a gain (G) of 1, 5 and 9 when the starting query is length 2. CSSxMI | G | Q∗ | A∗ | F∗ | C∗ | R∗
---|---|---|---|---|---|---
No Feedback | 1 | 1 | 7 | 0 | 96 | 0.014
FF-QC | 1 | 1 | 5 | 1 | 89 | 0.015
FA-QC | 1 | 1 | 5 | 1 | 123 | 0.012
FF-QS | 1 | 1 | 5 | 1 | 90 | 0.016
FA-QS | 1 | 1 | 2 | 1 | 80 | 0.015
No Feedback | 5 | 3 | 9 | 0 | 338 | 0.014
FF-QC | 5 | 1 | 11 | 5 | 209 | 0.023
FA-QC | 5 | 2 | 10 | 1 | 409 | 0.013
FF-QS | 5 | 2 | 10 | 1 | 278 | 0.017
FA-QS | 5 | 1 | 5 | 3 | 256 | 0.019
No Feedback | 9 | 7 | 10 | 0 | 791 | 0.011
FF-QC | 9 | 2 | 11 | 5 | 407 | 0.022
FA-QC | 9 | 4 | 11 | 1 | 842 | 0.011
FF-QS | 9 | 5 | 10 | 1 | 635 | 0.014
FA-QS | 9 | 1 | 9 | 5 | 541 | 0.016

The plots on the right hand side of Fig. 4, show how the two mixed initiatives
perform under the FA strategy. When query clarifications are offered after the
user assesses items, then the rate of gain initially is higher than the
baseline until A increases past three result items, then the strategy becomes
less effective and the rate of gain drops below baseline (top-right plot).
Interestingly, for query suggestions we see that rate of gain is much higher
when suggestions are taken afterwards, and it is only if the user assesses
more items per round of feedback does the rate of gain start to decrease and
tend towards the baseline (bottom-right plot). Here, we see the query
suggestions improve the initial query and bring more relevant information back
in subsequent assessment turns – but crucially assessing only a few items and
then providing feedback leads to the highest rate of gain for this mixed
initiative approach.

To directly compare the different combinations of search strategy and mixed
initiative, we have plotted the best performing combinations for each in Fig.
5. Here we can see that the FA-QC (with $F=1$) combination is clearly
inferior, while the FF-QS (with $F=1$) leads to a small increase over the
baseline. More interestingly, we see that FA-QS (with $F=3$) outperforms the
baselines and the other two combinations mentioned. However, FF-QC (with
$F=5$) leads to the highest rate of gains overall, if the user is willing to
assess five or more results per query. This suggests that there is no dominant
strategy/approach but two competing combinations. Thus, for the remainder of
our analysis, we focus on these two superior combinations: FF-QC ($F=5$), and
FA-QS ($F=3$).

Table 1 provides similar insights as described above, where we have listed the
configurations that lead to the higher rate of gain for different
strategies/approaches for three levels of gain. The table shows that as the
amount of gain desired increases then the FF-QC combination results in the
highest rate of gain.

(a) FF-QC (b) FA-QC (c) FF-QS (d) FA-QS (e)

Figure 4. The Rate of Gain (R) by the number of Assessments (A) for different
levels of feedback (F). Top: Query Clarification, Bot: Query Suggestion, Left:
Feedback First, and Right: Feedback After. For clarity, only F$\leq$5 and
Q$\leq$10 is shown.

###  6.3. Query Length

To explore our next research question on how the quality of queries influences
the choice of strategy and mixed initiative approach taken, we examined how
the rate of gain for each combination changed when we varied query length (and
consequently the retrieval performance), see. Fig. 6. In the plots, we can see
that as query length increases from L=1 to L=4, the rate of gain also
increases regardless of condition – which is to be expected (Belkin et al.,
2003).

In Fig. 6a we have plotted the rate of gain for FF-QC($F=5$). We can see that
the increase in query length leads to a higher rate of gain. However, when
compared to the no feedback condition, the rate of gain is similar when A is
less than 3, but after providing feedback leads to higher rates of gain (when
L=4).

A different story emerges in Fig. 6b (right), where we have plotted the rate
of gain for FA-QS ($F=3$). Here, when the length of the starting query is
short (L=1), obtaining feedback from query suggestions leads to dramatic
improvements in the rate of gain. However, if the starting query is longer
(L=4), then the benefit of obtaining feedback via query suggestions leads to
smaller increases in the rate of gain. Finally, as the number of assessments a
user is willing to make increases, the benefit of feedback rounds from query
suggestions diminishes and leads to a similar rate of gain as the no feedback
baseline. Essentially, going deeper mitigates conversational interactions.

Figure 5. The Rate of Gain (R) by the number of Assessments (A) for the F
value that yields the high rate of gain for each combination. FA-QC and FF-QS
are clearly inferior to FF-QC and FA-QS, respectively. For clarity, only
A$\leq$10 is shown.

###  6.4. Cost of Conversational Turns

To answer our final question, we explore whether changes to the costs affected
the viability of the strategy or approach. So far we have used costs grounded
by our user study, but what happens if the average cost associated with the
different conversational turns changes? To explore how this might affect
behaviours, we varied the cost of feedback, in two ways: (i) by halving it and
(ii) by doubling it. For FF-QC, Fig. 7a shows that as the feedback cost
decreases it leads to a higher rate of gain. And, as the cost of feedback
increases, we see that rate of gain decreases, making the combination less
attractive when A is low. For FA-QS, Fig. 7c shows that as feedback cost
decreases, the rate of gain also increases, and this makes the combination
worthwhile up until A is around 5-6 assessments. But when feedback cost
increases, then the viability of the combination diminishes quickly. And, in
fact, it eventually becomes worse than no feedback at all.

In terms of changes to query cost, when we reduce the cost of querying, then
the rate of gain for the baseline increases (as users reach relevant material
sooner) – and so we have updated the baselines in Fig. 7b and 7d. For FF-QC,
while previously providing clarifications resulted in a higher rate of gain,
the decrease in query costs, means that FF-QC is only effective when A is less
than 2, after that point re-querying results in a higher rate of gain. For FA-
QS, the suggestions still result in a higher rate of gain than the no feedback
baseline – but the difference between the feedback and no feedback condition
is considerably reduced – and as A gets larger the differences between becomes
smaller and smaller. Essentially, once queries become cheap enough, then
issuing a series of queries, even if some are poor, is likely to lead to a
higher rate of gain (as previously observed in (Keskustalo et al., 2009)
during session search), rather than trying to refine the query through
feedback.

Regardless of the combination, we found that if assessment cost decreases then
the rate of gain (R) increases, as less time is needed to extract relevant
information, and conversely as the assessment cost increase then the rate of
gain decreases. However, changing the cost of assessment didn’t impact when to
give feedback relative to the number of assessments (plots not shown).

(a) FF-QC; F=5

(b) FA-QS; F=3

Figure 6. The Rate of Gain (R) vs Assessments (A) for query lengths L=1 & L=4
with no feedback (F=0) & the F that yields the higher rate of gain. Left:
Feedback First - Query Clarifications, Right: Feedback After - Query
Suggestions. For clarity, only A$\leq$10 is shown.

##  7\. Discussion and Future Work

In this paper, we have explored how different CS strategies and different MI
approaches combine in the context of a text based CSA where we have simulated
CS sessions. In order to do so, we first built upon existing models of IIR to
develop a model of the CS process which explicitly includes the core
conversational concept of mixed initiative. From the model, we derived two
different CS strategies, which have been previously observed in conversational
settings. While these strategies reduced the evaluation space, it is still
largely intractable to explore all possible factors and so we focused on the
most salient (i.e. number of A, F and Q, given the different conditions).

With respect to the different conditions, we found that there was no dominant
CS strategy and MI approach combination. However, we did observe that certain
combinations were clearly inferior (e.g. FF-QS and FA-QC), while the choice of
combination FA-QC led to higher rates of gain when A was lower, whereas for
FF-QC higher rates of were observed when A was greater. Nonetheless, the
viability of these combinations was dependent upon the initial query
submitted, and the relative cost of giving feedback vs. the cost of querying.
In sum, if (i) the length/quality of the initial queries increases, (ii) the
cost of giving feedback increases, (iii) the cost of querying decreases, or
(iv) a combination of, then providing feedback regardless of combination
becomes less beneficial (resulting in a lower rate of gain), and it may even
be detrimental where the rate of gain drops below the no feedback / non-
conversational baseline. These findings begin to illuminate the complexities
and trade-offs involved in conversational search, where it is clear that
certain criteria need to be met for conversational search to be beneficial in
terms of the rate of gain.

It should be noted, however, that our findings need to be considered in
context. We evaluated one particular type of CSA – a chat/text based CSA like
those proposed in (Avula, 2017; Kaushik et al., [n. d.]; Zamani and Craswell,
2020) – where we employed the traditional IR evaluation approach in a
conversational setting. We also used simulation based methodology so that we
could begin to explore the large evaluation space (which would be near
impossible to do so within a user study). Even so, we could only explore a
subset of possibilities and focused on pure strategies with fixed rounds of
feedback, etc.. Nonetheless, by evaluating and comparing pure strategies
combined with the different mixed initiative approaches, we were still able to
observe the strengths and weaknesses of the combinations and better understand
the different trade-offs. In practice, however, it is clear that a mixture of
different strategies and approaches will be employed and required to optimize
the rate of the gain experienced during a CS session. As more interaction data
becomes available from deployed CSAs it will be possible to instantiate more
nuanced interaction models, and to evaluate other conversational search
settings where the costs and gains vary. Clearly, this would change the pay-
off dynamics associates with the different conversational turns – and so
evaluating different types of CSAs that, for example, try to surface relevant
information directly would invariably lead to different strategies evolving.
We have also made an assumption that CS should be as efficient as possible
(following Grice’s maxims of conversation (Grice, 1975)) and that the users of
CSAs and the CSAs will adapt/evolve to maximise the rate of gain (as per
Information Foraging Theory (Pirolli and Card, 1995)). However, it is possible
that the conversation itself has additional benefits leading to greater user
satisfaction which may not be captured by focusing solely on gain, cost or
rate measures. For example, in previous work, they found that asking a
relevant clarification increased user satisfaction in voice-only conversations
(Kiesel et al., 2018) and so this may lead to other trade-offs emerging with
satisfaction. Also, in this work, we solely relied on the TREC assessments. In
a more realistic experimental setup, one could compute gain based on the
amount of useful information given the agent’s response. But, these are
emerging challenges within the context of CS that need to be addressed through
the development of more fine grained test collections before we can evaluate
such scenarios.

In this paper, we have shown that the choice of search strategy and mixed
initiative depends upon a number of factors: the quality of the starting
query, the relative costs of querying vs. giving feedback, the number of
results the user is willing to assess, and the amount of gain desired. While
more work is needed to explore and investigate the effectiveness of different
CSA configurations, the methods they used, and the strategies they employ, we
have provided a model and framework for evaluating and simulating the
conversational search process in an offline/batch setting. This will enable
researchers to explore the complexities and trade-offs of design decisions
before developing and deploying them in practice.

Acknowledgements. This work was supported in part by the NWO Innovational
Research Incentives Scheme Vidi (016.Vidi.189.039), the NWO Smart Culture -
Big Data / Digital Humanities (314-99-301), the H2020-EU.3.4. - SOCIETAL
CHALLENGES - Smart, Green And Integrated Transport (814961), and in part by
the Center for Intelligent Information Retrieval. Any opinions, findings and
conclusions or recommendations expressed in this material are those of the
authors and do not necessarily reflect those of the sponsors.

(a) FF-QC; Changing $c_{f}$

(b) FF-QC; Changing $c_{q}$

(c) FA-QS; Changing $c_{f}$

(d) FA-QS; Changing $c_{q}$

Figure 7. The Rate of Gain (R) vs Assessments (A) as the query cost of
querying and the cost of feedback is varied. For clarity, only F$\leq$5 and
A$\leq$10 is shown.

## References

  * (1)
  * Aliannejadi et al. (2021) Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeffrey Dalton, and Mikhail Burtsev. 2021\.  Building and Evaluating Open-Domain Dialogue Corpora
with Clarifying Questions. In _EMNLP_.

  * Aliannejadi et al. (2020) Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, and Mikhail S. Burtsev. 2020\.  ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ).  _CoRR_ abs/2009.11352 (2020).
  * Aliannejadi et al. (2019) Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W. Bruce Croft. 2019.  Asking Clarifying Questions in Open-Domain Information-Seeking Conversations. In _SIGIR_. 475–484.
  * Allen et al. (1999) JE Allen, Curry I Guinn, and Eric Horvtz. 1999.  Mixed-Initiative Interaction.  _IEEE Intell. Syst._ 14, 5 (1999), 14–23.
  * Anand et al. (2019) Avishek Anand, Lawrence Cavedon, Hideo Joho, Mark Sanderson, and Benno Stein. 2019.  Conversational Search (Dagstuhl Seminar 19461).  _Dagstuhl Reports_ 9, 11 (2019), 34–83.
  * Avula (2017) Sandeep Avula. 2017\.  Searchbots: Using Chatbots in Collaborative Information-Seeking Tasks. In _SIGIR_. 1375\.
  * Azzopardi (2011) Leif Azzopardi. 2011\.  The Economics in Interactive Information Retrieval. In _SIGIR_. 15–24.
  * Azzopardi et al. (2007) Leif Azzopardi, Maarten de Rijke, and Krisztian Balog. 2007\.  Building Simulated Queries for Known-Item Topics: An Analysis using Six European Languages. In _SIGIR_. 455–462.
  * Azzopardi et al. (2018a) Leif Azzopardi, Mateusz Dubiel, Martin Halvey, and Jeffery Dalton. 2018a.  Conceptualizing Agent-Human Interactions during the Conversational Search Process. In _CAIR_.
  * Azzopardi et al. (2013) Leif Azzopardi, Diane Kelly, and Kathy Brennan. 2013\.  How Query Cost Affects Search Behavior. In _SIGIR_. 23–32.
  * Azzopardi et al. (2018b) Leif Azzopardi, Paul Thomas, and Nick Craswell. 2018b.  Measuring the Utility of Search Engine Result Pages: An Information Foraging Based Measure. In _SIGIR_. 605–614.
  * Azzopardi et al. (2019) Leif Azzopardi, Paul Thomas, and Alistair Moffat. 2019\.  cwl_eval: An Evaluation Tool for Information Retrieval. In _SIGIR_. 1321–1324.
  * Baskaya et al. (2012) Feza Baskaya, Heikki Keskustalo, and Kalervo Järvelin. 2012\.  Time Drives Interaction: Simulating Sessions in Diverse Searching Environments. In _SIGIR_. 105–114.
  * Belkin et al. (1995) Nicholas J Belkin, Colleen Cool, Adelheit Stein, and Ulrich Thiel. 1995.  Cases, Scripts, and Information-Seeking Strategies: On the Design of Interactive Information Retrieval Systems.  _Expert Syst. Appl._ 9, 3 (1995), 379–395.
  * Belkin et al. (2003) Nicholas J. Belkin, D. Kelly, G. Kim, J.-Y. Kim, H.-J. Lee, G. Muresan, M.-C. Tang, X.-J. Yuan, and C. Cool. 2003.  Query Length in Interactive Information Retrieval. In _SIGIR_. 205–212.
  * Bosetti et al. (2017) Gabriela Bosetti, Sergio Firmenich, Alejandro Fernández, Marco Winckler, and Gustavo Rossi. 2017\.  From Search Engines to Augmented Search Services: An End-User Development Approach. In _ICWE_. 115–133.
  * Braslavski et al. (2017) Pavel Braslavski, Denis Savenkov, Eugene Agichtein, and Alina Dubatovka. 2017.  What Do You Mean Exactly?: Analyzing Clarification Questions in CQA. In _CHIIR_. 345–348.
  * Carterette (2011) Ben Carterette. 2011\.  System Effectiveness, User Models, and User Utility: A Conceptual Framework for Investigation. In _SIGIR_. 903–912.
  * Croft (2019) W. Bruce Croft. 2019\.  The Importance of Interaction for Information Retrieval. In _SIGIR_. 1–2.
  * Croft and Thompson (1987) W. Bruce Croft and R. H. Thompson. 1987.  I${}^{\mbox{3}}$R: A New Approach to the Design of Document Retrieval Systems.  _J. Am. Soc. Inf. Sci._ 38, 6 (1987), 389–404.
  * Culpepper et al. (2018) J. Shane Culpepper, Fernando Diaz, and Mark D. Smucker. 2018\.  Research Frontiers in Information Retrieval: Report from the Third Strategic Workshop on Information Retrieval in Lorne (SWIRL 2018).  _SIGIR Forum_ 52, 1 (2018), 34–90.
  * Dalton et al. (2019) Jeffrey Dalton, Chenyan Xiong, and Jamie Callan. 2019\.  TREC CAsT 2019: The Conversational Assistance Track Overview. In _TREC_.
  * Dubiel et al. (2020) M. Dubiel, M. Halvey, L. Azzopardi, D. Anderson, and S. Daronnat. 2020.  Conversational Strategies: Impact on Search Performance in a Goal-Oriented Task Mateusz. In _CAIR_.
  * Grice (1975) Herbert P Grice. 1975\.  Logic and conversation.  In _Speech acts_. Brill, 41–58.
  * Hashemi et al. (2020) Helia Hashemi, Hamed Zamani, and W. Bruce Croft. 2020\.  Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search. In _SIGIR_. 1131–1140.
  * He and Young (2005) Yulan He and Steve J. Young. 2005.  Semantic Processing Using the Hidden Vector State Model.  _Comput. Speech Lang._ 19, 1 (2005), 85–106.
  * Huurnink et al. (2010) Bouke Huurnink, Katja Hofmann, Maarten de Rijke, and Marc Bron. 2010.  Validating Query Simulators: An Experiment Using Commercial Searches and Purchases. In _CLEF_. 40–51.
  * Järvelin and Kekäläinen (2017) Kalervo Järvelin and Jaana Kekäläinen. 2017.  IR Evaluation Methods for Retrieving Highly Relevant Documents.  _SIGIR Forum_ 51, 2 (2017), 243–250.
  * Järvelin et al. (2008) Kalervo Järvelin, Susan L. Price, Lois M. L. Delcambre, and Marianne Lykke Nielsen. 2008\.  Discounted Cumulated Gain Based Evaluation of Multiple-Query IR Sessions. In _ECIR_. 4–15.
  * Johnston et al. (2014) Michael Johnston, John Chen, Patrick Ehlen, Hyuckchul Jung, Jay Lieske, Aarthi Reddy, Ethan Selfridge, Svetlana Stoyanchev, Brant Vasilieff, and Jay Wilpon. 2014\.  MVA: The Multimodal Virtual Assistant. In _SIGDIAL_. 257–259.
  * Jordan et al. (2006) Chris Jordan, Carolyn R. Watters, and Qigang Gao. 2006\.  Using Controlled Query Generation to Evaluate Blind Relevance Feedback Algorithms. In _JCDL_. 286–295.
  * Kaushik et al. ([n. d.]) Abhishek Kaushik, Vishal Bhat Ramachandra, and Gareth J.F. Jones. [n. d.].  An Interface for Agent Supported Conversational Search. In _CHIIR_. 452–456.
  * Keskustalo et al. (2008) Heikki Keskustalo, Kalervo Järvelin, and Ari Pirkola. 2008\.  Evaluating the Effectiveness of Relevance Feedback based on a User Simulation Model: Effects of a user Scenario on Cumulated Gain Value.  _Inf. Retr._ 11, 3 (2008), 209–228.
  * Keskustalo et al. (2009) Heikki Keskustalo, Kalervo Järvelin, Ari Pirkola, Tarun Sharma, and Marianne Lykke. 2009.  Test Collection-based IR Evaluation Needs Extension Toward Sessions–A Case of Extremely Short Queries. In _AIRS_. 63–74.
  * Kiesel et al. (2018) Johannes Kiesel, Arefeh Bahrami, Benno Stein, Avishek Anand, and Matthias Hagen. 2018.  Toward Voice Query Clarification. In _SIGIR_. 1257–1260.
  * Kiseleva et al. (2016) Julia Kiseleva, Kyle Williams, Ahmed Hassan Awadallah, Aidan C Crook, Imed Zitouni, and Tasos Anastasakos. 2016.  Predicting User Satisfaction with Intelligent Assistants. In _SIGIR_. 45–54.
  * Krasakis et al. (2020) Antonios Minas Krasakis, Mohammad Aliannejadi, Nikos Voskarides, and Evangelos Kanoulas. 2020.  Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search. In _ICTIR_. 129–132.
  * Lipani et al. (2021) Aldo Lipani, Ben Carterette, and Emine Yilmaz. 2021\.  How Am I Doing?: Evaluating Conversational Search Systems Offline.  _ACM Trans. Inf. Syst._ 39, 4 (2021).
  * Lotze et al. (2021) Tom Lotze, Stefan Klut, Mohammad Aliannejadi, and Evangelos Kanoulas. 2021.  Ranking Clarifying Questions Based on Predicted User Engagement.  _CoRR_ abs/2103.06192 (2021).
  * Marchionini (1997) Gary Marchionini. 1997\.  _Information Seeking in Electronic Environments_.  Cambridge University Press, USA.
  * Maxwell and Azzopardi (2016) David Maxwell and Leif Azzopardi. 2016.  Agents, simulated users and humans: An analysis of performance and behaviour. In _CIKM_. 731–740.
  * Maxwell et al. (2015) David Maxwell, Leif Azzopardi, Kalervo Järvelin, and Heikki Keskustalo. 2015.  Searching and Stopping: An Analysis of Stopping Rules and Strategies. In _CIKM_. 313–322.
  * McTear (2002) Michael F. McTear. 2002\.  Spoken Dialogue Technology: Enabling the Conversational User Interface.  _ACM Comput. Surv._ 34, 1 (2002), 90–169.
  * Moffat et al. (2013) Alistair Moffat, Paul Thomas, and Falk Scholer. 2013\.  Users versus Models: What Observation Tells us about Effectiveness Metrics. In _CIKM_. 659–668.
  * Moffat and Zobel (2008) Alistair Moffat and Justin Zobel. 2008.  Rank-Biased Precision for Measurement of Retrieval Effectiveness.  _ACM Trans. on Inf. Sys._ 27, 1 (2008), 2:1–2:27.
  * Oddy (1977) R.N. Oddy. 1977\.  Information Retrieval Through Man‐Machine Dialogue.  _J. Documentation_ 33, 1 (1977), 1–14.
  * Pieraccini et al. (1992) Roberto Pieraccini, Evelyne Tzoukermann, Z. Gorelov, Jean-Luc Gauvain, Esther Levin, Chin-Hui Lee, and Jay Wilpon. 1992\.  A Speech Understanding System based on Statistical Representation of Semantics. In _ICASSP_. 193–196.
  * Pirolli and Card (1995) Peter Pirolli and Stuart K. Card. 1995.  Information Foraging in Information Access Environments. In _CHI_. 51–58.
  * Radlinski and Craswell (2017) Filip Radlinski and Nick Craswell. 2017.  A Theoretical Framework for Conversational Search. In _CHIIR_. 117–126.
  * Rao and Daumé (2018) Sudha Rao and Hal Daumé. 2018.  Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information. In _ACL (1)_. 2736–2745.
  * Sahib et al. (2012) Nuzhah Gooda Sahib, Anastasios Tombros, and Tony Stockman. 2012\.  A Comparative Analysis of the Information-Seeking Behavior of Visually Impaired and Sighted Searchers Nuzhah.  _J. Assoc. Inf. Sci. Technol._ 63, 2 (2012), 377–391.
  * Sekulic et al. (2021) Ivan Sekulic, Mohammad Aliannejadi, and Fabio Crestani. 2021\.  User Engagement Prediction for Clarification in Search. In _ECIR_. 619–633.
  * Sitter and Stein (1992) Stefan Sitter and Adelheit Stein. 1992.  Modeling the Illocutionary Aspects of Information-Seeking Dialogues.  _Inf. Process. Manag._ 28, 2 (1992), 165–180.
  * Smucker and Clarke (2012) Mark D. Smucker and Charles L. A. Clarke. 2012.  Time-based Calibration of Effectiveness Measures. In _SIGIR_. 95–104.
  * Spina et al. (2017) Damiano Spina, Johanne R. Trippas, Lawrence Cavedon, and Mark Sanderson. 2017.  Extracting Audio Summaries to Support Effective Spoken Document Search.  _J. Assoc. Inf. Sci. Technol._ 68, 9 (2017), 2101–2115.
  * Thomas et al. (2014) Paul Thomas, Alistair Moffat, Peter Bailey, and Falk Scholer. 2014.  Modeling Decision Points in User Search Behavior. In _IIiX_. 239–242.
  * Trippas et al. (2018a) Johanne R. Trippas, Damiano Spina, Lawrence Cavedon, Hideo Joho, and Mark Sanderson. 2018a.  Informing the Design of Spoken Conversational Search. In _CHIIR_. 32–41.
  * Trippas et al. (2018b) Johanne R. Trippas, Damiano Spina, Lawrence Cavedon, Hideo Joho, and Mark Sanderson. 2018b.  Informing the Design of Spoken Conversational Search: Perspective Paper. In _CHIIR_. 32–41.
  * Trippas et al. (2020) Johanne R. Trippas, Damiano Spina, Paul Thomas, Mark Sanderson, Hideo Joho, and Lawrence Cavedon. 2020.  Towards a Model for Spoken Conversational Search.  _Inf. Process. Manag._ 57, 2 (2020), 102162.
  * Vakulenko et al. (2019) Svitlana Vakulenko, Kate Revoredo, Claudio Di Ciccio, and Maarten de Rijke. 2019.  QRFA: A Data-Driven Model of Information Seeking Dialogues. In _ECIR_. 541–557.
  * Vtyurina et al. (2017) Alexandra Vtyurina, Denis Savenkov, Eugene Agichtein, and Charles L. A. Clarke. 2017.  Exploring Conversational Search With Humans, Assistants, and Wizards. In _CHI Extended Abstracts_. 2187–2193.
  * Wadhwa and Zamani (2021) Somin Wadhwa and Hamed Zamani. 2021.  Towards System-Initiative Conversational Information Seeking. In _DESIRES_.
  * Walker et al. (2001) Marilyn A. Walker, Rebecca J. Passonneau, and Julie E. Boland. 2001.  Quantitative and Qualitative Evaluation of Darpa Communicator Spoken Dialogue Systems. In _ACL_. 515–522.
  * Wambua et al. (2018) Muuo Wambua, Stefania Raimondo, Jennifer Boger, Jan Polgar, Hamidreza Chinaei, and Frank Rudzicz. 2018\.  Interactive Search through Iterative Refinement. In _CAIR_.
  * Yan et al. (2016) Rui Yan, Yiping Song, and Hua Wu. 2016.  Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System. In _SIGIR_. 55–64.
  * Yang et al. (2018) Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Jun Huang, and Haiqing Chen. 2018\.  Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems. In _SIGIR_. 245–254.
  * Zamani and Craswell (2020) Hamed Zamani and Nick Craswell. 2020.  Macaw: An Extensible Conversational Information Seeking Platform. In _SIGIR_. 2193–2196.
  * Zamani et al. (2020a) Hamed Zamani, Susan T. Dumais, Nick Craswell, Paul N. Bennett, and Gord Lueck. 2020a.  Generating Clarifying Questions for Information Retrieval. In _WWW_. 418–428.
  * Zamani et al. (2020b) Hamed Zamani, Gord Lueck, Everest Chen, Rodolfo Quispe, Flint Luu, and Nick Craswell. 2020b.  MIMICS: A Large-Scale Data Collection for Search Clarification. In _CIKM_. 3189–3196.
  * Zamani et al. (2020c) Hamed Zamani, Bhaskar Mitra, Everest Chen, Gord Lueck, Fernando Diaz, Paul N. Bennett, Nick Craswell, and Susan T. Dumais. 2020c.  Analyzing and Learning from User Interactions for Search Clarification. In _SIGIR_. 1181–1190.
  * Zhang and Balog (2020) Shuo Zhang and Krisztian Balog. 2020.  Evaluating Conversational Recommender Systems via User Simulation. In _KDD_. ACM, 1512–1520.
  * Zhang et al. (2018) Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, and W. Bruce Croft. 2018.  Towards Conversational Search and Recommendation: System Ask, User Respond. In _CIKM_. 177–186.


# Recent results on Central Exclusive Production
with the STAR detector at RHIC††thanks: Presented at Diffraction and Low-$x$
2018 workshop, 26 August 2018 to 1 September 2018, Reggio Calabria, Italy,
https://indico.cern.ch/event/713101/

Rafał Sikora for the STAR Collaboration This work was partially supported by
the Polish National Science Centre grant No. UMO-2015/18/M/ST2/00162 and by
the AGH UST grant No. 15.11.220.717/28 for young researchers within subsidy of
Ministry of Science and Higher Education. AGH University of Science and
Technology,
Faculty of Physics and Applied Computer Science,
al. A. Mickiewicza 30, 30-059 Kraków, Poland

###### Abstract

We present preliminary differential fiducial cross sections on Central
Exclusive Production (CEP, $pp\to pXp$) of two opposite-charge mesons
($X=\pi^{+}\pi^{-}$, $K^{+}K^{-}$) in midrapidity region with small squared
four-momentum transferred from forward protons,
$0.03<|t_{1}|,|t_{2}|<0.2~{}\textrm{GeV}^{2}$. The process was measured in the
STAR experiment at the Relativistic Heavy Ion Collider (RHIC) in proton-proton
collisions at the center-of-mass energy $\sqrt{s}~{}=~{}200$ GeV.

13.25.Jx, 13.30.Eg, 13.85.-t, 13.87.Ce, 14.40.-n, 24.10.Ht

##  1 Introduction

The STAR experiment at RHIC in recent years puts a lot of effort into the
measurements of high-energy particle diffraction processes [1, 2], such as
Central Exclusive Production (CEP) in proton-proton collisions. In this
process, the protons stay intact after the interaction, while the fraction
part of their momentum is transferred into the neutral system measured by the
central detector. The transverse momenta of the protons gained from this
scattering process makes them deviate from the nominal beamline trajectory
which enables their detection in forward detectors placed close to the
beamline.

The dominant CEP mechanism expected at studied center-of-mass energy is the
Double Pomeron Exchange (DPE) - a fusion of two Pomerons in the Regge
framework. The gluon-rich environment of the reaction is considered as
suitable for the search of the gluon bound states (glueballs), which are
predicted by the QCD theory but not yet confirmed.

##  2 Experimental setup

Thanks to the wide acceptance of installed detectors, measurement of CEP in
the STAR experiment is possible. The heart of the STAR detector, the Time
Projection Chamber (TPC), is used to reconstruct momentum and specific energy
loss ($dE/dx$) of charged particles in pseudorapidity range $|\eta|<1$. The
Time-Of-Flight (TOF) system surrounding TPC is used to extend particle
identification capabilities by measuring particle velocity. Last but not
least, there are Roman Pot (RP) detectors which allow efficient triggering and
measurement of diffractively scattered protons. Detailed description of STAR
forward proton detectors can be found in Ref. [3].

##  3 Event selection

The presented preliminary results were obtained using dedicated data collected
in year 2015 during 11-week period of $p+p$ collisions at $\sqrt{s}=200$ GeV.
The trigger logic required signals in RP scintillators on both sides of the
STAR central barrel to ensure the detection of scattered protons. In addition,
at least 2 trigger-level hits in the TOF detector were required to ensure
presence of at least 2 charged particles in the TPC. Additionally, a veto was
performed on signal in small Beam-Beam Counter (BBC) scintillator tiles
($3.3<|\eta|<5.0$) and Zero-Degree Calorimeters (ZDCs) to assure no overlap
with pile-up interactions which would fill the rapidity gaps expected in DPE
between final state protons and central diffractive system.

Figure 1: Distribution (raw counts) of the $x$\- (horizontal axis) and
$y$-component (vertical axis) of the forward proton momentum in events passing
all CEP event selection cuts, except forward proton fiducial region
requirement which is drawn with solid black lines. Parts of the distribution
at positive and negative $p_{y}$ correspond to protons measured in the
detectors placed above and below the beamline, respectively.

Analysis of CEP events started with selection of good quality proton tracks
reconstructed in RPs on both sides of the STAR detector. These protons were
further required to have transverse momentum ($p_{x}$, $p_{y}$) in the
fiducial region that guaranteed high geometrical acceptance, and overall
trigger, and track reconstruction efficiency (Fig. 1). The fiducial region was
chosen based on detailed simulations of diffractive proton propagation from
the interaction point through collider lattice (beampipe, magnets) to RP
detectors, implemented in Geant4.

Figure 2: (left) Distribution (raw counts) of the total transverse momentum
and (right) invariant mass of $\pi^{+}\pi^{-}$ in the fiducial region defined
in the plots. The same-sign background events are also shown. Filled violet
histogram in the invariant mass plot represents the opposite-sign background
determined from the data using $p_{\text{T}}^{\text{miss}}$ extrapolation from
the background-only region ($p_{\text{T}}^{\text{miss}}>200~{}\text{MeV}$) to
0 in bins of $m(\pi^{+}\pi^{-})$.

Next, exactly 2 opposite-sign TPC tracks matched with hits in TOF were
required, both originating from the only primary vertex in an event and
passing track quality cuts with $p_{\text{T}}>0.2$ GeV and $|\eta|<0.7$. The
$z$-position of the vertex was limited to $|z_{\text{vtx}}|<80$ cm to ensure
high geometrical acceptance for the central tracks in the entire fiducial
phase space. The $z$-position of the vertex could also be reconstructed by
measuring the time difference of the two protons reconstructed from RPs
(resolution $\sigma_{\Delta z_{\text{vtx}}}=$ 11 cm), which was compared to
that measured in TPC - difference of $3\sigma_{\Delta z_{\text{vtx}}}$ was
allowed.

To further suppress the residual backgrounds, a veto on minimum ionizing
particle signal in large BBC tiles ($2.1<|\eta|<3.3$) was imposed, as well as
veto on additional TOF hits without matching TPC tracks. This mainly removed
higher multiplicity central diffraction events with some particles not
reconstructed or produced outside of TPC and TOF acceptance.

Identification of particles was performed using combined information from TPC
and TOF. A pair of tracks was recognized as either $\pi^{+}\pi^{-}$,
$K^{+}K^{-}$ or $p\bar{p}$ if the $dE/dx$ of both tracks was consistent with
given hypothesis and mass of particle calculated from the time of hit in TOF
(assuming equal masses of positive and negative charge particle) also
supported that hypothesis. If a pair was recognized as species other than
$\pi^{+}\pi^{-}$, an additional cut on track $p_{\text{T}}$ was imposed:
$>0.3$ GeV for $K^{\pm}$ and $>0.4$ GeV for $p(\bar{p})$.

After the event selection described above, one can clearly see the signal
events among remaining background in the distribution of total transverse
momentum of all measured particles, $p_{\text{T}}^{\text{miss}}$ (Fig. 2,
left). A final cut on total transverse momentum,
$p_{\text{T}}^{\text{miss}}<75$ MeV, was employed to improve the exclusivity
of the system. The cut value was determined by the finite resolution of
transverse momentum originated from the angular divergence of proton beams.

Fig. 2 (right) shows raw distribution of the invariant mass of
$\pi^{+}\pi^{-}$ system in CEP process. One can see that the background does
not exceed a few percent level. Overall numbers of selected $\pi^{+}\pi^{-}$,
$K^{+}K^{-}$ and $p\bar{p}$ pairs equals 84 000, 1 000 and 70, respectively.

##  4 Results

Events selected with the cut flow described above were subjected to background
subtraction and model-independent efficiency corrections derived from Monte
Carlo (MC) simulations of the STAR detector response to particles traversing
the central detector and protons passing through RP detectors. A technique of
full MC event embedding into zero-bias data was utilized to reproduce all real
environment effects. Closure tests were also conducted on MC events to verify
that distributions of reconstructed and corrected observables are consistent
with those at the hadron level. Finally, the differential fiducial cross
sections were calculated, presented in Figs. 3-5. For each result a
conservative systematic uncertainty was estimated and drawn with the gray band
in the panel below main plot. Systematic uncertainties are strongly correlated
between data points. Luminosity uncertainty (10%) was also included.

The invariant mass spectra of exclusively produced $\pi^{+}\pi^{-}$ and
$K^{+}K^{-}$ pairs are presented in Fig. 3, together with the predictions of
some non-resonant pair production models implemented in MC event generators
[4, 5, 6]. Models qualitatively describe $d\sigma/dm(\pi^{+}\pi^{-})$ up to
$\sim 0.7$ GeV, which is consistent with the expectation of no narrow
resonances in this mass range. The peak in data below 1 GeV followed by a
sharp drop of the cross section is presumably due to $f_{0}(980)$ interference
with production amplitude opposite in phase to $\pi^{+}\pi^{-}$ continuum. The
peak between 1-1.5 GeV probably originated from $f_{2}(1270)$ with potential
mixture of $f_{0}(1370)$, while the sharp drop of the cross section at 1.5 GeV
might be due to $f_{0}(1500)$ interference with the remaining states.

Figure 3: (left) Differential fiducial cross section as a function of
invariant mass of $\pi^{+}\pi^{-}$ and (right) invariant mass of $K^{+}K^{-}$
compared with some models of non-resonant CEP.

Figure 4: (left) Differential fiducial cross section as a function of the
angular separation between scattered forward protons in the transverse plane
$\Delta\varphi$ and (right) invariant mass of $\pi^{+}\pi^{-}$ for two ranges
of $\Delta\varphi$.

In the $K^{+}K^{-}$ mass spectrum, the key feature is the prominent peak
starting at $1.5~{}\textrm{GeV}$ which could be the $f_{0}(1500)$ state mixed
with $f^{\prime}_{2}(1525)$ and $f_{0}(1710)$. There is also a structure in
the $f_{2}(1270)$ mass region, however this part could be well described by
the non-resonant model only.

In Fig. 4 (left) we show differential fiducial cross section for CEP of
$\pi^{+}\pi^{-}$ as a function of the azimuthal angle between scattered
protons, $\Delta\varphi$, which is equal to the incident angle between objects
exchanged by protons in the $xy$-plane. In Fig. 4 (right) we present
$d\sigma/dm(\pi^{+}\pi^{-})$ in two ranges of $\Delta\varphi$. One can clearly
notice significant differences between these subsets. The low cross section
below $\sim$0.7 GeV for $\Delta\varphi>90^{\circ}$ is a kinematic effect
connected with the low transverse momentum of $\pi^{+}\pi^{-}$ pair (pions do
not reach requested $p_{\text{T}}>$ 0.2 GeV). However, above that mass the
difference in shape is driven only by the dependence of production amplitudes
of various states on dynamics of DPE.

Figure 5: (left) Differential fiducial cross section as a function of the
rapidity of $\pi^{+}\pi^{-}$ pair and (right) sum of squared four-momentum
carried by objects exchanged by protons in CEP of $\pi^{+}\pi^{-}$.

In Fig. 5 we introduce differential fiducial cross section as a function of
the central pair rapidity and sum of Mandelstam’s $t$ variables. The data can
not be described by continuum models which confirms large contribution from
resonances in CEP. The differences may also indicate significant role of
absorption effects which influence the cross sections.

##  5 Summary

The preliminary STAR results on CEP of $\pi^{+}\pi^{-}$ and $K^{+}K^{-}$ in
proton-proton collisions at $\sqrt{s}=$ 200 GeV with detected forward
scattered protons have been presented and discussed. In general, models of
non-resonant CEP can not describe the data, which agrees with expectation of
significant role of resonance production.

## References

  * [1] W. Guryn, Proceedings of this conference (Diffraction and Low-$x$ 2018)
  * [2] Ł. Fulek, Proceedings of this conference (Diffraction and Low-$x$ 2018)
  * [3] R. Sikora, AIP Conf. Proc. 1819, 040012 (2017), arXiv:1611.07823 [nucl-ex].
  * [4] L. A. Harland-Lang, V. A. Khoze, M. G. Ryskin, Eur. Phys. J. C 74, 2848 (2014), arXiv:1312.4553 [hep-ph].
  * [5] J. Chwastowski, R. Kycia, R. Staszewski, J. Turnau, arXiv:1411.6035 [hep-ph].
  * [6] R. Ciesielski, K. Goulianos, PoS ICHEP2012, 301 (2013), arXiv:1205.1446 [hep-ph].