# Pre-training Text-to-Text Transformers for Multiview Contextual Commonsense
Inference in Dialogues

First Author
Affiliation / Address line 1
email@domain
&Second Author
Affiliation / Address line 1
email@domain
Siqi Shen${}^{\bigstar\includegraphics[height=5.20058pt]{fig/umich.png}}$
Deepanway
Ghosal${}^{\bigstar\includegraphics[height=8.67424pt]{fig/declarelogo.pdf}}$
Navonil Majumder${}^{\includegraphics[height=8.67424pt]{fig/declarelogo.pdf}}$
Henry Lim${}^{\includegraphics[height=8.67424pt]{fig/declarelogo.pdf}}$
Rada Mihalcea${}^{\includegraphics[height=5.20058pt]{fig/umich.png}}$ Soujanya
Poria${}^{\includegraphics[height=8.67424pt]{fig/declarelogo.pdf}}$
${}^{\includegraphics[height=5.20058pt]{fig/umich.png}}$ University of
Michigan, USA
${}^{\includegraphics[height=8.67424pt]{fig/declarelogo.pdf}}$ DeCLaRe Lab,
Singapore University of Technology and Design, Singapore
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
{navonil_majumder@,henry_lim<EMAIL_ADDRESS>
CICEROv2 is available at: https://declare-lab.github.io/CICERO

# Multiview Contextual Commonsense Inference: A New Dataset and Task

First Author
Affiliation / Address line 1
email@domain
&Second Author
Affiliation / Address line 1
email@domain
Siqi Shen${}^{\bigstar\includegraphics[height=5.20058pt]{fig/umich.png}}$
Deepanway
Ghosal${}^{\bigstar\includegraphics[height=8.67424pt]{fig/declarelogo.pdf}}$
Navonil Majumder${}^{\includegraphics[height=8.67424pt]{fig/declarelogo.pdf}}$
Henry Lim${}^{\includegraphics[height=8.67424pt]{fig/declarelogo.pdf}}$
Rada Mihalcea${}^{\includegraphics[height=5.20058pt]{fig/umich.png}}$ Soujanya
Poria${}^{\includegraphics[height=8.67424pt]{fig/declarelogo.pdf}}$
${}^{\includegraphics[height=5.20058pt]{fig/umich.png}}$ University of
Michigan, USA
${}^{\includegraphics[height=8.67424pt]{fig/declarelogo.pdf}}$ DeCLaRe Lab,
Singapore University of Technology and Design, Singapore
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
{navonil_majumder@,henry_lim<EMAIL_ADDRESS>
CICEROv2 is available at: https://declare-lab.github.io/CICERO

###### Abstract

Multiview contextual commonsense inference is the task of determining
commonsense explanations around the events in a dyadic dialogue, where
multiview refers to the characteristic that there can be multiple plausible
but independent inferences. Producing a coherent and non-trivial explanation
requires awareness of the dialogue's structure and how an event is grounded in
the context, yet there is a lack of high-quality resources dedicated to the
task. In this work, we create CICEROv2, a dataset consisting of 8,351
instances from 2,379 dialogues, containing multiple human-written answers for
each contextual commonsense inference question, representing a type of
explanation on cause, subsequent event, motivation, and emotional reaction. We
show that the inferences in CICEROv2 are of higher semantic diversity than
other contextual commonsense inference datasets. In addition, we propose a
collection of pretraining objectives, including concept denoising and
utterance sorting, to help adapt language models for the multiview contextual
commonsense inference task. Evaluation results show the effectiveness of the
pretraining stage, as there is a universal improvement in accuracy for all
inference types.

##  1 Introduction

Perhaps unwittingly, commonsense is a key part of daily conversations. Rather
than being explicit, interlocutors usually rely on shared context and
commonsense knowledge to make sense of the inbound utterances and respond as
succinctly as possible to maximize information flow Grice (1975). The scope of
this shared context, however, is quite often broad enough to span beyond the
scope of the given conversation. Understanding various dimensions of such
conversations for NLP systems is thus rather challenging without the aid of
commonsense-based reasoning. Some of the useful dimensions, such as cause,
subsequent events, and motivation behind some given utterance, can be
extracted from the explicit context. Otherwise, the broader context that fits
the explicit context must be imagined. Either way, commonsense knowledge must
be employed with the context in mind to broaden the context if necessary and
arrive at a fitting explanation. Inferring such explanations for various
dimensions with the context and commonsense-based reasoning is called
contextual commonsense inference. An accurate understanding of dialogues
achieved through contextual commonsense inference can assist in meaningful
indexing, filtering, and searching of the copious amount of conversational
content available on the internet. Tasks like affect analysis and relation
extraction in dialogues may also benefit from such explanations.

To this end, the CICERO dataset Ghosal et al. (2022) collects five dimensions
of contextual commonsense inferences for utterances in dialogues. However, for
each present dimension-utterance pair, only one human-annotated explanation is
collected. The remaining explanations, if any, are picked using adversarial
filtering Zellers et al. (2018a) from a set of fine-tuned language model-
generated explanations. These auto-generated explanations are both lexically
and semantically very close to the human-annotated explanation. This
contradicts the intuitive multiview nature of these explanations, where
multiple disparate explanations for the same event may exist (see Fig. 1).
CICEROv2 seeks to address this issue by collecting multiple distinct human-
annotated explanations, leading to the enrichment of the downstream models for
contextual commonsense inference task.

The availability of multiple correct answers brings the need for methods that
can simultaneously select multiple correct answers from a mixture of correct
and incorrect answers given a context. Ghosal et al. (2022) shows that given a
context, selecting two correct answers is harder than selecting just one. On
CICERO, T5-Large attains an Exact Match (EM) score of 95% on the single answer
selection task but this score drops to 20% on the multiple answer selection
task. Models need to encode rich commonsense knowledge to solve this task due
to its hardness. In this work, we attempt to encode commonsense knowledge to a
large pre-trained language model T5-Large by continuing training it on a
dialogue-level commonsense dataset CICERO Ghosal et al. (2022) using a set of
commonsense-aware pre-training objectives. Large pre-trained language models,
such as GPT-2 Radford et al. (2019) and T5 Raffel et al. (2020b), seem
attractive frameworks to solve contextual commonsense inference task. Through
fine-tuning, these models have become state of the art in several natural
language understanding tasks, such as SuperGLUE Wang et al. (2019).
Additionally, being trained on several hundreds of GB of text may have endowed
these models with much commonsense knowledge Petroni et al. (2019).

However, the fine-tuning approach may not suffice for tasks with limited
training samples. Nonetheless, previous work Gururangan et al. (2020); Zhou et
al. (2021a) has shown that, prior to fine-tuning, pre-training with objectives
catered to the target tasks may improve performance on such tasks. Following
this intuition, we propose a set of self-supervised pre-training objectives to
adapt the language models for the contextual commonsense inference task,
specifically addressing the task of multi-choice answer selection.

Thus, our contribution in this paper is twofold: _i)_ we curate CICEROv2,
containing multiple distinct contextual commonsense inferences per dimension,
and _ii)_ we propose a set of pre-training objectives for contextual
commonsense inference that improves over the vanilla fine-tuning by about 1.9%
for the multi-choice answer selection task, defined on both CICERO and
CICEROv2 datasets.

##  2 Primer on CICERO

The dialogues in CICERO Ghosal et al. (2022) are sourced from three different
datasets: DailyDialog Li et al. (2017), MuTual Cui et al. (2020), and DREAM
Sun et al. (2019). All dialogues are dyadic and their inherent nature is
particularly conducive to qualitatively rich utterance-level inferences. These
annotated inferences are categorized into five dimensions: cause, subsequent
event, prerequisite, motivation, and emotional reaction. The tasks proposed on
these inferences require contextual understanding, multi-utterance reasoning,
and commonsense knowledge.

In addition to introducing CICERO, Ghosal et al. (2022) also defines a multi-
choice answer selection task (MCQ), where the original annotation is
considered as the primary correct answer. The candidates for the remaining
correct and incorrect answers are generated using fine-tuned T5 models Raffel
et al. (2020a). Adversarial filtering Zellers et al. (2018a) is applied to
these candidates to identify the hard-to-distinguish answers, which are
manually labeled as correct or incorrect.

##### Drawbacks of CICERO.

The automatically-generated and labeled-as-correct answers are the only
sources of secondary correct answers in the CICERO dataset. In total, close to
15% of the instances contain multiple correct answers (inferences). We
empirically analyzed these instances and found that the adversarial filtering
algorithm favors the selection of alternate answers that are lexically close
to the primary correct answer. As such, both correct and incorrect answers
bear a relatively high degree of token-level and semantic similarity with each
other as indicated in Table 2 in terms of BLEU, ROUGE-L, CIDER and semantic-
similarity metrics. This belies the multiview nature of commonsense-based
explanations, where multiple either independent or related explanations of the
same event may exist. This is demonstrated in Fig. 1 where the target
utterance ``_I don't think so. I know I've put on weight this winter._ '' can
be a consequence of multiple possible events. Particularly, the event of
weight gain can be caused by lack of physical activity and exercise or
unhealthy diet or perhaps both. There are myriad of other possible factors
that may contribute to the weight gain, such as disease, but those multitudes
of possibilities or views are not captured in CICERO.

Figure 1: Demonstration of multiple possible contextual explanations through
multiple commonsense-based mechanisms.

##  3 CICEROv2

To address the drawbacks highlighted earlier, we introduce CICEROv2, to
improve the generalization ability of the models trained on this data.
CICEROv2 contains commonsense inferences from target utterances of dyadic
dialogues sampled from CICERO. A human annotator is given a dialogue with a
target utterance and asked a question about the target utterance. The
annotator writes multiple distinct correct answers and two or more incorrect
answers for the question.

We start by sampling (dialogue, target, question) triplets from CICERO. For
these instances, we show the original correct answer from CICERO to the
annotators to avoid duplication. The annotators write at least one more
correct and at least two incorrect answers that are semantically distinct from
each other and the answer from CICERO. This original answer and the newly
written answer(s) constitute the set of answers for these instances.

We also sample new (dialogue, target, question) triplets, not present in
CICERO. The annotators write at least two correct answers and two incorrect
answers for these instances.

The above strategy ensures that all instances in CICEROv2 have at least two
correct and two incorrect answers.

###  3.1 Annotation Instructions

##### Guidelines for Writing Correct Answers.

We instruct the annotators to write context-congruent correct answers that are
grammatically sound and concise sentences. The answers may contain some
important terms from the context and must be commonsense-based, factual, and
plausible.

##### Guidelines for Writing Incorrect Answers.

The incorrect answers are also grammatically correct and concise but must
contradict some information in the dialogue. Incorrect answers should contain
some important terms from the context and must be commonsense-based and
factual. Annotators were instructed not to write incorrect answers that are
clearly outlandish in the given context.

We also ask the annotators to write sufficiently diverse and distinct correct
and incorrect answers. This diversity may stem from token-level differences,
semantic differences, or various likely speculative scenarios around the given
context. Human-written diverse incorrect answers is a major contribution in
CICEROv2, which is absent CICERO. We discuss the diversity of answers in
CICERO and CICEROv2 in more detail in Section 3.3.

We collect inferences across four different dimensions in CICEROv2: subsequent
event, cause, motivation, and emotional reaction w.r.t the target.
_Prerequisite_ dimension from CICERO is skipped as the annotators found it
difficult to distinguish from _cause_ during annotation training. The
annotators are asked to write correct and incorrect answer(s) to the questions
representing each of the four inference dimensions. We expand on the
annotation instructions outlined by Ghosal et al. (2022) for answer writing.
Both correct and incorrect answers may describe either an _overt_ or a
_speculative_ scenario, as illustrated in CICERO. An overt answer is
explicitly or implicitly present in the dialogue context. However, when a
dialogue does not explicitly or implicitly hold the answer to a question about
a particular target, the answer is speculated within the dialogue context
imagined and broadened using commonsense and world knowledge.

The following illustrates the questions and possible correct and incorrect
answer(s) for the (dialogue, target) pair shown in Fig. 2.

##### Q1.

What subsequent event happens (overt) or could happen (speculative) following
the Target? The annotators write about the event that happens or could happen
following the target. They are also made aware that at times such subsequent
events could be triggered by the target itself.

CICERO Correct Answer: The speaker made delicious banana cookies. Incorrect
Answers: i) The speaker is making a chocolate cake. ii) The speaker was baking
a cake.

CICEROv2 Correct Answer: The speaker threw the leftover oranges into the
rubbish bin. Incorrect Answers: i) The listener requests to taste the orange
cookies. ii) The listener started to make orange chocolate cookies.

##### Q2.

What is the event that directly causes (overt) or could cause (speculative)
Target? The annotators consider the events antecedent to the target that cause
or likely cause the target.

CICERO Correct Answer: The speaker was making banana cookies. Incorrect
Answers: i) The speaker is making a chocolate cake. ii) The speaker was baking
a cake.

CICEROv2 Correct Answers: i) It is too difficult to process the orange pulp.
ii) The orange smell doesn't match well with chocolate. Incorrect Answers i)
The orange smell matches much better with chocolate compared with banana. ii)
The speaker loves the taste of orange and the texture of its pulp.

Figure 2: A (dialogue, target) pair; the utterance with the red border is the
target.

##### Q3.

What is the emotion or basic human drive that motivates or could motivate
Target? We ask the annotators to consider the basic human drives and needs of
the speaker of the target utterances. The basic human drives include food,
water, clothing, rest, safety, friends, relationships, enjoyment, etc. Do or
may any of the human drives/states of mind/emotional feelings motivate the
target?

CICERO Answers: Instance not present.

CICEROv2 Correct Answers: i) The speaker wants the cookies to be delicious.
ii) The oranges were not sweet enough for the cookies. Incorrect Answers: i)
The speaker prefers spicy cookies. ii) The speaker wants to use the leftover
pears before they go bad.

##### Q4.

What is the possible emotional reaction of the listener: A (or B)? What could
be the possible emotional reaction of the listener to the target? The
annotators capture the appropriate emotion of the listener using the emotion
terms listed in the Appendix in Table 7 using verbatim or related words (e.g.,
anxious, confused, interested).

CICERO Correct Answer: The listener is excited to eat the cookies. Incorrect
Answers i) The listener is excited to eat the salad. ii) The listener is
excited to eat the muffins instead.

CICEROv2 Correct Answer: The listener feels pity that they cannot have orange
cookies. Incorrect Answers i) The listener is happy to taste orange cookies.
ii) The listener is annoyed by the banana smell.

###  3.2 Sampling of Dialogues and Targets

From the (dialogue, target, question) triplets in CICERO, the following
criteria is used to subsample a set of triplets for annotation:

  * •

The target utterance must contain at least one non-stop verb word and more
non-stop words than stop words.

  * •

If the _dialogue_ is from DailyDialog, then the dialogue-act label of the
target utterance must either be directive or commissive Li et al. (2017).

These sampled target utterances often describe some action or activity, which
the annotators found easier to annotate across the four question types.
Overall, 17% of the correct answer annotations in CICEROv2 also appear in
CICERO. However, there is no overlap between the incorrect answers in the two
datasets. Crucially, CICEROv2 contains all manually annotated and semantically
diverse set of commonsense-based correct and incorrect answers that capture
distinct perspectives or views. We expand upon the diversity of the answers
next.

###  3.3 Diversity of Answers

Description | CICEROv2 | CICERO
---|---|---
# Dialogues / # Instances |  |
DailyDialog | 1,118 / 3,973 | 2,113 / 4,344
MuTual | 1,011 / 3,384 | 929 / 1,715
DREAM | 250 / 994 | 516 / 1,386
Total | 2,379 / 8,351 | 3,558 / 7,445
# Dialogues with # Instances |  |
$<$ 4 | 1,377 | 3,057
4 $\leq*\leq$ 8 | 919 | 493
$>$ 8 | 83 | 8
Avg. # of Correct Answers | 2.40 | 2.49
Instances with # Correct Answers |  |
$=$ 2 | 5,066 | 4,985
$=$ 3 | 3,260 | 1,552
$>$ 3 | 25 | 908
Question Types in Train / Validation / Test |  |
Cause | 1,227 / 189 / 243 | 1,301 / 381 / 514
Subsequent Event | 2,196 / 618 / 793 | 1,193 / 568 / 759
Motivation | 1,457 / 330 / 480 | 455 / 163 / 194
Reaction | 561 / 116 / 141 | 234 / 105 / 116
Prerequisite | - | 1,010 / 201 / 251

Table 1: Statistics of CICEROv2. We report the numbers only for the multiple
correct answer subset in CICERO.

Data (x, y) | BLEU1 | BLEU2 | BLEU4 | ROUGE-L | CIDER | Sem-Sim
---|---|---|---|---|---|---
v1 (C, C) | 0.7082 | 0.6340 | 0.4817 | 0.7323 | 0.2918 | 0.7974
v1 (I, I) | 0.5966 | 0.5036 | 0.3442 | 0.6119 | 0.7434 | 0.7120
v1 (C, I) | 0.6797 | 0.6028 | 0.4565 | 0.7016 | 0.1268 | 0.7355
v2 (C, C) | 0.3265 | 0.1966 | 0.0501 | 0.3533 | 0.0028 | 0.5934
v2 (I, I) | 0.3455 | 0.2164 | 0.0625 | 0.3738 | 0.0009 | 0.5425
v2 (C, I) | 0.3367 | 0.2214 | 0.0685 | 0.3614 | 0.3421 | 0.5097

Table 2: (x, y) indicates source-target pair. v1, v2, C, I indicate CICERO,
CICEROv2, correct answer set, and incorrect answer set, respectively. We show
the instance-level average similarity between pairs of (correct, correct),
(incorrect, incorrect), and (correct, incorrect) answers in CICERO and
CICEROv2.

Answers in CICEROv2 are significantly more diverse than CICERO. We observe
this trend among both correct and incorrect answers. As such, CICEROv2
provides much richer and diversified multiview commonsense inferences than
CICERO. We show a comparative example of annotations in CICERO and CICEROv2 in
Table 10.

We compute the instance-level average of BLEU Papineni et al. (2002), ROUGE
Lin (2004), CIDEr Vedantam et al. (2015), and semantic similarity among all
(correct, correct), (incorrect, incorrect) and (correct, incorrect) answer
pairs in Table 2. We use the all-mpnet-base-v2 model Reimers and Gurevych
(2019) to compute the semantic similarity. All scores are reported between
0-1, with a higher score indicating more similarity. The numbers reported in
Table 2 clearly indicate that answers in CICERO are significantly less
diverse. We also conclude that annotations in CICEROv2 provide a superior
quality of multiview commonsense inferences. Similar to Ghosal et al. (2022),
we carry out a quality assurance stage on CICEROv2, details of which can be
found in Appendix B.

##  4 DIALeCT

We propose DIALogue-level Commonsense Transformer – DIALeCT, a pretrained
transformer for commonsense inference in dialogues. It is a model trained on a
variety of dialogue-related tasks, which help the model better leverage the
structural information from the dialogues. The model can be used as the
initial weight for further downstream task finetuning and have the capability
of making zero-shot inference on its own.

The pretraining tasks include choosing the correct options illustrated,
recovering corrupted input, sorting, and generation based on concepts
augmented input. All the pretraining tasks are built based on only the
training set of CICERO to avoid information leaking from seeing the dialogues
in the test set. We describe details for each training objective in the
following sections.

###  4.1 Problem Formulation

Given a dialogue $\mathcal{D}$ consists of $n$ utterances:
$[u_{1},u_{2},...,u_{n}]$, our task is to predict the correct answers from a
set of choices for questions on a target utterance $u_{i}$ in CICERO as
illustrated in Section 3: cause (c), effect (e), motivation (m), prerequisite
(p), and reaction (r). We denote the questions as $Q=[Q^{j}]$, where
$j=[0,1,..,4]$ corresponds to the relation type being asked. The annotated
answers on target utterance $u_{i}$ are represented as
$A_{i}=[a_{i}^{j}]=[c_{i},e_{i},m_{i},p_{i},r_{i}]$, for each aforementioned
question type respectively. Each $a_{i}^{j}$ consists of multiple choices,
among which at least two are correct answers. For the pretraining, we use
$a_{i}^{j}$ to refer to one of the correct answers if not indicated otherwise.
We denote the non-stopword nouns and verbs for either utterance $u_{i}$ or the
corresponding answer as concept $c_{i}$. Note that not all five questions are
annotated for each target utterance in CICERO. Hence, for a particular target
utterance, $A_{i}$ and $Q$ contain only a subset of the question types, making
the value of $j$ no more than four.

###  4.2 Pre-training objectives

We propose a set of objectives to train the model in a text-to-text manner.
The input usually consists of a combination of the prompt text denoting the
task referred to as $p$, the concatenation of utterances in the dialogue
$\mathcal{D}$ referred to as context $x$, and objective-specific information
detailed in the following section. Different parts of the input are
concatenated to form the input sequence, separated by special tokens and text
indicating the parts. We give details on the input formats and prompts used
for all the pre-training objectives in Table 13.

####  4.2.1 Primary Objectives (PO)

Primary objectives train open-ended text generation without any set of options
to choose from, as in the contextual commonsense inference task:

  1. (i)

Given context $x$, target utterance $u_{i}$, and question $Q^{j}$, generate
the corresponding answer $a_{i}^{j}$.

  2. (ii)

Given context $x$, question $Q^{j}$, and its answer $a_{i}^{j}$, generate the
corresponding target utterance $u_{i}$.

  3. (iii)

Given context $x$, target utterance $u_{i}$, question $Q^{j}$, answer
$a_{i}^{j}$, and question or another type $Q^{k}$, generate the corresponding
answer $a_{i}^{k}$.

####  4.2.2 Single Correct Answer Objectives (SCAO)

These objectives ask the model to generate the right choice from the given
options or a closed set of relations, with access to the dialogue context.

  1. (i)

Given context $x$, target utterance $u_{i}$, question $Q^{j}$, multiple answer
choices $\bar{a}_{i}^{j}$, generate the correct answer $a_{i}^{j}$. The answer
choices $\bar{a}_{i}^{j}$ includes correct answer $a_{i}^{j}$ and incorrect
answers $a_{i}^{j-}$. We concatenate the question, target, context, and answer
choices with separators to form the input.

  2. (ii)

Given context $x$, target utterance $u_{i}$, answer $a_{i}^{j}$, generate the
question type of $Q^{j}$. We concatenate the answer, target, and context to
form the input. The output is one of the five question type strings: cause,
effect, motivation, prerequisite, or reaction.

  3. (iii)

Given context $x$, answer $a_{i}^{j}$, question $Q^{j}$, a pool of possible
target utterances $\bar{u}_{i}$, choose the correct target utterance $u_{i}$.
The pool includes correct target utterance $u_{i}$ and three other utterances
$u_{i}^{-}$ randomly sampled from the same dialogue.

####  4.2.3 Concept-Based Objectives (CO)

These objectives train to reconstruct a sentence from the set of concepts it
contains, and generate the answer or target from the concepts in the target or
answer, respectively. The concepts are selected based on the part-of-speech
tags parsed by Spacy111https://spacy.io/ after removing stop words.

  1. (i)

Given context $x$, question $Q^{j}$, concepts $c_{i}^{j}$ from answer
$a_{i}^{j}$, generate the target utterance $u_{i}$. We use the concatenation
of a template question and context as the input.

  2. (ii)

Given context $x$, concepts $c_{i}$ from target utterance $u_{i}$, question
$Q^{j}$, answer $a_{i}^{j}$, generate the target utterance $u_{i}$. Following
a strategy similar to the previous case, we use $x,a_{i}^{j},c_{i},Q^{j}$
along with a template question to form the input.

  3. (iii)

Given context $x$, question $Q^{j}$, and concepts $c_{i}$ from target
utterance $u_{i}$, generate the answer $a_{i}^{j}$. We concatenate the
question, concepts, and context to form the input.

  4. (iv)

Given context $x$, target utterance $u_{i}$, question $Q^{j}$, and concepts
$c_{i}^{j}$ from answer $a_{i}^{j}$, generate the answer $a_{i}^{j}$. We
concatenate the question, target, concepts, and context to form the input.

####  4.2.4 Denoising Objectives (DO)

These objectives train to restore and order the corrupted concepts in the
target utterance or answer. Corruption is performed by randomly changing the
order of the concepts, and randomly removing one concept in the original
utterance or answer. A similar concept order recovery has previously been
explored by Zhou et al. (2021b).

  1. (i)

Given context $x$, target utterance $u_{i}$, question $Q^{j}$, corrupted
concepts $\bar{c}_{i}^{j}$ for answer $a_{i}^{j}$, generate correct concepts
$c_{i}^{j}$.

  2. (ii)

Given context $x$, question $Q^{j}$, answer $a_{i}^{j}$, corrupted concepts
$\bar{c}_{i}$ for utterance $u_{i}$, generate correct concepts $c_{i}$.

####  4.2.5 Sorting-Based Objectives (SO)

Sorting-based objectives require the model to be aware of the order of the
utterances and the order of questions asked in the dialogue.

  1. (i)

We consider the following precedence order of the relations: $c\rightarrow
p\rightarrow m\rightarrow e\rightarrow r$. Now, given context $x$ and a
randomly ordered subset of answers $\hat{a}$ from $A$, the objective is to
generate the sorted order of $\hat{a}$ according to utterance location and
relation precedence. The output to be generated is formulated according to
indices of answers in the subset $\hat{a}$. For instance, if
$\hat{a}=[r_{5},p_{2},e_{0},c_{0},m_{2}]$, the output to generate would be 3 2
1 4 0, denoting the sorted order $c_{0}\rightarrow e_{0}\rightarrow
p_{2}\rightarrow m_{2}\rightarrow r_{5}$.

  2. (ii)

Given a randomly ordered set of utterances $\hat{u}$ from $\mathcal{D}$,
identify the correct order. For example if $\hat{u}=[u_{3},u_{1},u_{2}]$, the
output to be predicted is the string 1 2 0, assuming indexing starts from 0.

##  5 Experiments

We evaluate the effectiveness of DIALeCT on commonsense inference tasks with
the multi-choice question answering (MCQ) format under various settings, where
we compare the performance of models finetuned on the MCQ task based on
DIALeCT with the baselines.

###  5.1 Experimental Setup

##### Pretraining.

We pretrain DIALeCT using T5-large as the backbone (770M parameters). We
initialize the parameters with the checkpoint released by Raffel et al.
(2020a) and continue pretraining in a text-to-text manner instead of span
filling. We use the Adafactor Shazeer and Stern (2018) optimizer with a weight
decay of 0.005 and a learning rate of 1e-5. Note that Adafactor significantly
reduces the memory footprint for conversational tasks with long text input. We
train the model for 75000 steps with a batch size of 16. The training takes
around 22 hours on two A40 GPUs.

##### Finetuning.

We finetuned the model based on either DIALeCT or T5-large. We use the
Adafactor optimizer during pretraining with a learning rate of 3e-5. All
finetuning experiments are run for 5 epochs with five different random seeds.
Each trial takes 30 minutes on an A40 GPU.

##### Evaluation Metrics

We use macro-F1 and Exact Match to evaluate the performance of the models.

Model | Finetuned on |  Avg Macro F1 | Exact Match
---|---|---|---
Cause | Subseq | Prereq | Motiv | Reaction | Average
T5Large | CICERO | 0.7001 | 0.2521 | 0.2358 | 0.2430 | 0.3258 | 0.3258 | 0.2566
DIALeCTLarge | CICERO | 0.7066 | 0.2736 | 0.2560 | 0.2457 | 0.3539 | 0.3420 | 0.2754
T5Large | CICEROv2 | 0.8795 | 0.6552 | 0.7148 | - | 0.7587 | 0.7243 | 0.7195
DIALeCTLarge | CICEROv2 | 0.8863 | 0.6905 | 0.7388 | - | 0.7537 | 0.7614 | 0.7380

Table 3: Performance of DIALeCT on CICERO and CICEROv2.

###  5.2 Overall Results on CICERO

We evaluate DIALeCT with MCQ from the CICERO dataset it pretrained on. Table 3
shows the performance on the MCQ task. We find that DIALeCT improves the
performance compared to the baseline on all metrics except recall. For the
exact match, there is around 2% universal improvement for all inference types,
indicating that the pretraining is not limited to a certain type of
commonsense inference. The results suggest that, although having the same
access to dialogue context and question-answer pairs from the same dataset,
the pretraining helps exploit the information in the dataset.

###  5.3 Transferability of Pretraining

To further investigate if the performance boost comes from merely seeing the
questions and choices in advance. We test DIALeCT on newly collected CICEROv2.
Table 3 shows that DIALeCT outperform the T5-large baseline on all metrics
again. There is a similar trend of improvement across inference types for
exact matches. The results show information learned in DIALeCT generalize to
MCQ samples drawn from a different distribution. Interestingly, despite seeing
answers of CICERO during pre-training, the performance of DIALeCT on CICERO is
worse than its performance on CICEROv2. We think this could be due to the high
lexical overlap and semantic similarity between correct and incorrect answers
in CICERO (as shown in Table 2) that might cause confusion in easily finding
the decision boundary. As a result, both T5-large and DIALeCT perform poorly
to predict multiple correct answers in CICERO.

Objectives |  Avg Macro F1 | Exact Match
---|---|---
Cause | Subseq | Prereq | Motiv | Reaction | Average
T5-Large | 0.7001 | 0.2521 | 0.2358 | 0.2430 | 0.3258 | 0.3258 | 0.2566
All | 0.7066 | 0.2736 | 0.2560 | 0.2457 | 0.3539 | 0.3420 | 0.2754
-PO | 0.7069 | 0.2964 | 0.2534 | 0.2722 | 0.3660 | 0.3190 | 0.2841
-SCAO | 0.6963 | 0.2613 | 0.2797 | 0.2324 | 0.3419 | 0.3276 | 0.269
-CO | 0.7096 | 0.2867 | 0.2587 | 0.2563 | 0.3505 | 0.3276 | 0.2803
-DO | 0.7036 | 0.2737 | 0.2530 | 0.2430 | 0.3505 | 0.2931 | 0.2703
-SO | 0.7090 | 0.2834 | 0.2609 | 0.2656 | 0.3626 | 0.3074 | 0.2815
Ensemble | - | 0.2964 | 0.2797 | 0.2722 | 0.3660 | 0.3420 | 0.3112

Table 4: Ablation study on CICERO. Reported results are the average of five
different runs. The Ensemble model selects the best-performing ablated model
for a particular relation.

Objectives |  Avg Macro F1 | Exact Match
---|---|---
Cause | Subseq | Motiv | Reaction | Average
T5-Large | 0.8795 | 0.6552 | 0.7148 | 0.7587 | 0.7243 | 0.7195
All | 0.8863 | 0.6905 | 0.7388 | 0.7537 | 0.7614 | 0.7380
-PO | 0.8854 | 0.7105 | 0.7303 | 0.7480 | 0.7619 | 0.7354
-SCAO | 0.8840 | 0.6790 | 0.7362 | 0.7516 | 0.7333 | 0.7320
-CO | 0.8900 | 0.7065 | 0.7408 | 0.7613 | 0.7714 | 0.7443
-DO | 0.8866 | 0.7023 | 0.7315 | 0.7550 | 0.7738 | 0.7378
-SO | 0.8867 | 0.6872 | 0.7273 | 0.7662 | 0.7666 | 0.7360
Ensemble | - | 0.7105 | 0.7408 | 0.7662 | 0.7738 | 0.7478

Table 5: Ablation study on CICEROv2. Reported results are the average of five
different runs.

###  5.4 Ablation Study of Pretraining Objectives

For a fair comparison, we remove a group of pretraining objectives for each
setting and pretrain the model with the exact same set of hyper-parameters,
including the random seeds, all for five epochs. Table 4 shows that all the
ablation models still outperform the baseline, meaning that there is at least
more than one group of helpful objectives. Removing the Single Correct Answer
Objectives i.e., SCAO causes the largest drop among all metrics, suggesting it
carries essential information. On contrary, removing Primary Objectives and
Sorting Based Objectives leads to slightly higher metrics. One plausible
explanation is that the gap in the input format for these objectives causes
trouble for later finetuning. For example, Sorting Objectives ask the model to
predict a sequence of integers, which may be confused with the multiple-choice
marker. The results for CICEROv2 is shown in Table 5. It holds the same
conclusion that all ablation models perform better than the baseline. It is
also interesting that the Concept Objective i.e., CO ablation group gets the
highest performance on most of the metrics, suggesting that the concepts from
CICERO may misalign with the ones in CICEROv2.

###  5.5 Performance Analysis

##### Cross-Dataset Performance.

Train | Test | Exact Match
---|---|---
Cause | Subseq | Prereq | Motiv | Reaction | Average
v1-four | v1-four | 0.3307 | 0.3254 | 0.2948 | 0.4794 | 0.431 | 0.3457
v2-four | 0.2451 | 0.253 | 0.2669 | 0.3557 | 0.3448 | 0.2694
$\Delta$ | 0.0856 | 0.0724 | 0.0279 | 0.1237 | 0.0862 | 0.0763
v2-four | v2-four | 0.5934 | 0.5858 | - | 0.7244 | 0.6214 | 0.6302
v1-four | 0.1203 | 0.3062 | - | 0.4948 | 0.2857 | 0.3321
$\Delta$ | 0.4731 | 0.2796 | - | 0.2296 | 0.3357 | 0.2981

Table 6: Cross-dataset results (avg. of five runs) of DIALeCT; v1-four and
v2-four stand for CICERO and CICEROv2, respectively, culled to have four
options per sample.

Table 6 shows cross-dataset adaptability of DIALeCT. To circumvent the
influence of the variability of answer counts in CICERO and CICEROv2, we cull
the samples of both datasets to have exactly four answers. For each sample
with more than four answers, two correct answers are randomly picked without
replacement, and then two more answers are randomly chosen from the rest. This
results in at least two correct answers per sample. As expected, both cross-
dataset transfers lead to diminished performance due to the starker difference
in distribution between training and test set. Interestingly, the performance
drop of 29.81% for CICERO to CICEROv2 transfer is far more severe than the
drop of 7.63% for CICEROv2 to CICERO transfer. This observation strongly
implies that CICEROv2 allows for a much more robust cross-dataset transfer
than CICERO. This is likely a consequence of the larger diversity of answers
in the training samples of CICEROv2, as indicated in Section 3.3. Another
observation is the performance improvement (7.03%) and degradation (10.78%) on
in-dataset transfer for culled CICERO and CICEROv2, respectively. This is
indicative of the strong influence of negative samples over the overall
performance of DIALeCT on both datasets.

Figure 3: The performance of finetuned models trained with fewer samples.

##### Performance with Fewer Training Examples.

To assess our proposed objectives' efficacy in the low-resource setting, we
compare the fine-tuning performance of DIALeCT with T5-Large using different
fragments of the training data. As shown in Fig. 3, DIALeCT consistently
attains better exact match accuracy than the T5-Large baseline on both CICERO
and CICEROv2. It can be seen that the performance improvement by DIALeCT is
more significant under the low resource setting. When finetuned with 20% of
the training data, DIALeCT offers over 5% performance boost on both datasets,
compared with around 2% for the full dataset. This indicates that DIALeCT
might be endowed with some commonsense knowledge through its pre-training
using our proposed objectives. As a result, DIALeCT does not require much
training data before attaining a decent performance. Note that, although
building the pre-training objectives relies on the training set of CICERO, the
training set of CICEROv2 is not used, and thus can be considered as a "true
low resource setting". In contrast, the baseline T5-Large model needs more
training data before obtaining a good fine-tuning performance. Based on these
observations, we may conjecture that T5-Large lacks the required commonsense
knowledge that DIALeCT encodes in its parameters.

##  6 Related Works

The area of commonsense reasoning has received significant attention recently,
with the introduction of several new benchmarks Zellers et al. (2018b); Talmor
et al. (2019); Bisk et al. (2020). The benchmarks target evaluation of
commonsense across various dimensions - causality Roemmele et al. (2011),
social commonsense with question answering Sap et al. (2019), abduction
Bhagavatula et al. (2020), etc. Language models specifically trained for
commonsense reasoning across these dimensions have also been proposed Lourie
et al. (2021). Despite the progress in those directions, the multiview aspect
of question answering and commonsense reasoning, particularly, has been an
under-explored area. Recently, Zhu et al. (2020) proposed a dataset for
extractive multiple-span question answering; Qin et al. (2021) introduced a
dataset for temporal reasoning in dialogues with multiple correct answers
satisfying certain temporal properties. Ghosal et al. (2022) introduced the
CICERO dataset for dialogue reasoning with contextual commonsense inference.
Although the dataset contains multiple speculative inferences, there are
certain shortcomings as inferences are less diverse, and not all inferences
are human-written. We motivate our work against this aspect and present a
dataset with significantly richer and more diverse multiview commonsense
inferences from dialogues, all of which are human-written.

##  7 Conclusion

We introduce CICEROv2, a human-written dataset for distinct multiview
commonsense inferences in dialogues. The dataset contains $\sim$8.3k instances
from $\sim$2.3k dialogues across four commonsense dimensions – cause,
subsequent event, motivation, and reaction. We also propose DIALeCT, which is
pre-trained on a collection of dialogue understanding objectives. We evaluate
it on the multiview commonsense inference task and analyze its performance
across various settings.

##  8 Limitations

Our model DIALeCT can only perform the answer selection task (MCQ). Wherein
the commonsense inference generation as proposed in Ghosal et al. (2022) is
more challenging which DIALeCT can not solve. Besides, DIALeCT requires heavy
computing power for pre-training and fine-tuning. As a consequence, it can not
be deployed on mobile devices with very low computational power. On the other
hand, our proposed CICEROv2 only contains inferences across four different
commonsense dimensions – cause, subsequent event, motivation, and reaction.
Hence, models e.g., DIALeCT trained on CICEROv2 could be limited in their
capacity to infer other types of commonsense relations.

## Ethics Statement

The annotators for CICEROv2 were hired through a data annotation service. The
compensation was derived based on the country of residence of the annotators,
as deemed by the company. The study has been categorized as ``exempt'' by the
IRB. Annotators were strictly asked not to write any toxic content (hateful or
offensive toward any gender, race, sex, or religion). They were asked to
consider gender-neutral settings in dialogues whenever possible.

The source dialogue datasets – DailyDialog, MuTual, and DREAM are high-quality
multi-turn dialogue datasets manually annotated by experts in dialogue,
communication theory, and linguistics. All three datasets have been
extensively used and studied in the natural language processing literature.
The three source datasets and our annotations in CICEROv2 do not contain any
personal data or any information that can uniquely identify individual people
or groups.

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##  Appendix A Annotation of Emotional Reaction

The annotators capture the appropriate emotion of the listener using the
emotion terms listed in Table 7 using verbatim or related words, to write the
answer for the question What is the possible emotional reaction of the
listener: A (or B)?

##  Appendix B Quality Assurance of CICEROv2

The dataset quality is ensured with the following steps:

  * •

Initially, we sample $30$ random dialogues and manually annotate all the
questions in those. Each annotator is then evaluated on those dialogues and is
selected for the annotation task if 95% of his/her annotations are approved by
us.

  * •

We constantly review and provide feedback to the annotators during the
annotation process. Annotators are also instructed to amend their answers.

  * •

Upon completion of the annotation, we employ three additional annotators who
manually check the annotated samples and score their acceptability. These
annotators reached a consensus for approving 96.2% of these samples. The
samples not bearing majority agreement were removed from the dataset. The
statistics of the annotated dataset are shown in Table 1. A number of
annotated examples from CICEROv2 are also shown in Table 10.

Admiration | Affection | Afraid | Angry
---|---|---|---
Annoyed | Anticipating | Anxious | Apprehensive
Ashamed | Awe | Awkwardness | Boredom
Calmness | Caring | Confident | Confusion
Content | Craving | Devastated | Disappointed
Disgusted | Eagerness | Embarrassed | Encouragement
Enthusiasm | Excited | Faithful | Fear
Furious | Grateful | Gratitude | Guilty
Happy | Hopeful | Impressed | Interest
Jealous | Joyful | Lonely | Nostalgic
Prepared | Proud | Relief | Romance
Sad | Satisfaction | Sentimental | Surprised
Terrified | Trusting |  |

Table 7: List of possible emotional reactions of the listener.

Description | # Instances | Percentage
---|---|---
# Dialogues / # Inferences |  |
DailyDialog | 3,280 / 30,509 | 57.82 / 57.34
MuTual | 1,640 / 14,207 | 28.91 / 26.70
DREAM | 753 / 8,488 | 13.27 / 15.95
Total | 5,673 / 53,204 | –
# Dialogues with # Inferences |  |
less than 10 | 3,140 | 55.35
between 10-20 | 2,518 | 44.39
between 21-30 | 15 | 0.26
Avg. # Inferences per Dialogue | 9.38 | –
Instances with # Correct Answers |  |
only 1 | 45759 | 86.01
only 2 | 4985 | 9.37
$>$ 2 | 2460 | 4.62
Inference Types in Train / Validation / Test |  |
Cause | 10,386 / 3,060 / 3,071 | 33.06 / 28.10 / 28.18
Subsequent Event | 6,617 / 4,021 / 4,050 | 21.06 / 36.93 / 37.16
Prerequisite | 7,501 / 1,347 / 1,396 | 23.87 / 12.37 / 12.81
Motivation | 4,412 / 1,420 / 1,401 | 14.04 / 13.04 / 12.86
Reaction | 2,502 / 1,040 / 980 | 7.96 / 9.55 / 8.99

Table 8: Statistics of CICERO Ghosal et al. (2022).

Group | # Instances | Sub-group |  Sub-group # Instances
---|---|---|---
PO | 107,198 | (i), (ii) |  31,418
(iii) |  44,362
SCAO | 94,254 | (i) - (iii) |  31,418
CO | 125,672 | (i) - (iv) |  31,418
DO | 60,302 | (i) |  31,369
(ii) |  28,933
SO | 6,953 | (i) |  3,476
(ii) |  3,477
Total | 394,379 | - |  -

Table 9: The number of instances for each group and corresponding sub-groups
of objective functions as described in Section 4.2. The number of training
instances in CICERO is 31,418, which is also the number of instances in some
of the sub-groups.

A ($u_{1}$): What's that smell? A ($u_{2}$): Are you making a chocolate cake?
A ($u_{3}$): I smell something different, peers? B ($u_{4}$): No, I'm making
chocolate banana cookies. B ($u_{5}$): At first I was going to use the oranges
, but I think these will taste better.
---
Target - $u_{4}$; Question: Motivation; Correct Answers in CICEROv2: i) The
speaker has leftover chocolate and bananas and wants to consume them quickly.
ii) The speaker likes chocolate sweets. Incorrect Answers in CICEROv2: i) The
speaker wants to make the kitchen smelly to stop the listener entering. ii)
The speaker is hungry and chocolate is not filling enough.
Target - $u_{4}$; Question: Subsequent Event; Correct Answers in CICERO: i)
The listener will request his friend to taste the cookies he prepared just
now. Correct Answers in CICEROv2: i) The speaker asks the listener to pass the
spatula to her. Incorrect Answers in CICERO: i) The listener will ask his
friends to taste the cake he prepared just now. ii) The listener will request
his friends to taste the chocolate cake he prepared just now. Incorrect
Answers CICEROv2: i) The speaker invites the speaker to taste the orange
cookies. ii) The listener asks the speaker to get out of the kitchen then
takes over the cookies.
Target - $u_{5}$; Question: Cause; Correct Answers in CICERO: i) The speaker
was making banana cookies. Correct Answers in CICEROv2: i) It is too difficult
to process the orange pulp. ii) The orange smell doesn't match well with
chocolate. Incorrect Answers in CICERO: i) The speaker is making a chocolate
cake. ii) The speaker was baking a cake. Incorrect Answers in CICEROv2: i) The
orange smell matches much better with chocolate compared with banana. ii) The
speaker loves the taste of orange and the texture of its pulp.
Target - $u_{5}$; Question: Emotional Reaction; Correct Answers in CICERO: i)
The listener is excited to eat the cookies. Correct Answers in CICEROv2: i)
The listener feels pity that she cannot have orange cookies. Incorrect Answers
in CICERO: i) The listener is excited eats the salad. ii) The listener is
excited to eat the muffins instead. Incorrect Answers in CICEROv2: i) The
listener is happy to taste orange cookies. ii) The listener is annoyed by the
banana smell.

Table 10:  Annotated examples in CICERO and CICEROv2 marked with the target
utterance and the question type. The first (dialogue, target, question)
instance is not present in CICERO. We show the incorrect answers in CICERO for
the other three instances. For these instances, the first correct answer is
the primary human written answer in CICERO. Incorrect answers in CICERO are
significantly less diverse than CICEROv2.

##  Appendix C Additional Details on the Pre-training

We use the CICERO dataset to pre-train DIALeCT. Detailed statistics of this
CICERO dataset are presented in Table 8. The objective-wise statistics of the
training dataset used in pre-training DIALeCT are reported in Table 9.

##  Appendix D Annotation Details

We recruited 32 student helpers who are undergraduate students studying
computer science and fluent in speaking and writing English. These students
have knowledge of Artificial Intelligence. The annotators were paid 7.5 USD
per hour which is a standard rate for hiring student helpers at our
university. In total, the total cost of the annotation was 2955 USD.

Figure 4: The distribution of Rouge1-P for correct/incorrect answers in CICERO
and CICEROv2. CICERO has more samples with a zero ROUGE score, and both
correct and incorrect answers from CICEROv2 have a slightly higher average
ROUGE score than its counterpart.

##  Appendix E Additional Performance Analysis

##### Impact of Lexical Overlap of Answers and Context.

We use ROUGE-1 precision as the lexical similarity measure between answers and
context. For each sample in the training set, we calculate the average ROUGE
score for its answers having dialogue context as the reference of this
calculation. The distribution of ROUGE scores is shown in Fig. 4.

Train | Test | Rc | Ri | |Rc-Ri| | Average Exact Match
---|---|---|---|---|---
All | All - PO | All - SCAO | All - CO | All - DO | All - SO
CICERO | CICEROlowr | 0.0103 | 0.1970 | 0.1867 | 23.87 | 26.66 | 24.51 | 25.37 | 22.79 | 25.80
CICEROhighr | 0.4715 | 0.2062 | 0.2653 | 28.18 | 30.14 | 26.47 | 29.41 | 30.39 | 28.92
CICEROv2 | CICEROv2-lowr | 0.0665 | 0.1519 | 0.0854 | 75.36 | 72.14 | 72.86 | 73.93 | 74.64 | 75.36
CICEROv2-highr | 0.4961 | 0.4488 | 0.0473 | 72.30 | 71.51 | 70.53 | 73.08 | 71.12 | 70.92

Table 11: The lexical similarity between answers and context also impact
models' performance significantly. lowr and highr denote low rouge precision
and high rouge precision groups respectively. Rc and Ri denote the average
ROUGE score of correct and incorrect answers, respectively. Figure 5: Learning
Curve of the Pretraining. The validation loss plateaus and starts to increase
after 50k. The best-pretrained model is selected based on its performance on
the downstream task of multiple-answer selection.

We set the lower quartile of the average ROUGE score of training samples as
the low threshold and the upper quartile as the high threshold. Based on the
two thresholds, we then filter the samples in the test set into low-ROUGE and
high-ROUGE groups.

Table 11 shows that the models perform better on the high-ROUGE group of
CICERO. This follows the intuition that samples from the high-ROUGE group are
easier to predict as they overlap more with the context.

That is not the case for CICEROv2, where all models perform better in the low-
ROUGE group. Upon deeper inspection, we find that the models' performance
might be influenced by the gap between the overlap, quantified by the ROUGE
score, of the correct ($R_{c}$) and incorrect answers ($R_{i}$), instead of
the absolute overlap of the correct answers. For the high-ROUGE group of
CICEROv2, the incorrect answers have an average ROUGE score of 0.45, which is
very close to the score of 0.49 for the correct answers. That may make the
separation between the correct and incorrect answers difficult.

Note that the incorrect answers in CICERO have almost the same average ROUGE
scores across both groups, as they are generated automatically. The correct
answers in the high-ROUGE group of CICERO have an average ROUGE score of 0.47,
and the incorrect answers in the same group have an average ROUGE score of
0.20. On the other hand, in the low-ROUGE group, correct and incorrect answers
have an average ROUGE score of 0.01 and 0.19, respectively. We surmise, as
compared to the lower-ROUGE group, the larger gap between the ROUGE scores of
the correct and incorrect answers in the high-ROUGE group of CICERO aids
DIALeCT to attain better performance in this group.

##### Pre-training Steps Required to Converge.

Fig. 5 depicts the number of training steps required to converge.

A ($u_{1}$): Did I do well on my test? B ($u_{2}$): Do you want to know the
honest answer? A ($u_{3}$): Why wouldn't I want to know? B ($u_{4}$): You had
pretty bad scores. A ($u_{5}$): Exactly what do you mean by bad? B ($u_{6}$):
You failed. A ($u_{7}$): How'd I fail it? B ($u_{8}$): There are a couple of
reasons why you didn't pass. A ($u_{9}$): What did I do wrong? B ($u_{10}$):
To sum it all up, you really just don't know how to drive. A ($u_{11}$):
Thanks. Will I be able to take a retest? B ($u_{12}$): Sure you can , in about
two and a half weeks.
---
Target - $u_{11}$; Question: What is or could be the life goal of the target?
Inference: The speaker is hopeful of getting a re-test.
Target - $u_{12}$; Question: What is or could be the physical requirement of
the target? Inference: The speaker has a good driving record.
Target - $u_{12}$; Question: What is or could be the intention of the target?
Inference: The speaker is encouraging the listener to re-appear in the driving
test.
A ($u_{1}$): David, do you like ice cream? B ($u_{2}$): Yes I do, a lot! A
($u_{3}$): Well, why don't we go get some today? B ($u_{4}$): Sorry, I can not
make it today as I have some other plans.
Target - $u_{4}$; Question: What is the goal of the speaker in the target?
Inference: The speaker has to attend a meeting.
Target - $u_{4}$; Question: What is the emotion of the speaker in the target?
Inference: The speaker is disappointed as he is unable to go for ice cream.
A ($u_{1}$): David, do you like ice cream? B ($u_{2}$): Yes I do, a lot! A
($u_{3}$): Well, why don't we go get some today? B ($u_{4}$): I can't wait.
Target - $u_{4}$; Question: What is the goal of the speaker in the target?
Inference: The speaker and david are craving for ice cream.
Target - $u_{4}$; Question: What is the emotion of the speaker in the target?
Inference: The speaker is excited to go to the ice cream shop.

Table 12: Examples of zero-shot question types and inferences.

##### Examples of Generated Outputs from the Pre-training Stage.

We provide examples of inputs, ground truth and generated outputs by DIALeCT
in Table 13.

##### Examples of Generated Output for Multiview Contextual Commonsense
inference.

We provide a few examples where DIALeCT makes the correct predictions while
the baseline model makes commonsense mistakes in Table 14. For example, in the
first dialogue, the model needs to guess what will happen next after the
speaker complains Aspirin is not strong enough. The baseline model mistakenly
selects option 4, suggesting the listener to visit the emergency room to get
medicines. Similarly, in the second example, the baseline model predicts that
a thief pulled out a knife will ask if he was okay as the next movement. In
the following example, the waiter is confirming if the guest wants to book the
room which requires the room to be not occupied and again contradicts option 4
predicted by the baseline model. DIALeCT makes such commonsense mistakes much
less compared to the baseline. The last example illustrates a case where
DIALeCT makes a prediction that contains the words upstairs but fails to
understand the relative spatial information of the speaker and listener and as
a result, makes a commonsense mistake. It suggests that the model's ability to
do inference still needs to be improved.

##### Zero-shot Transfer with DIALeCT

We examine if DIALeCT is capable of performing zero-shot inferences on unseen
questions beyond the pre-training corpus. We show some examples of such
inferences in Table 12. DIALeCT provides correct inferences for life goal,
physical requirement, and intention dimension in the first dialogue for
different target utterances. The second and third dialogue contexts are
constructed in a way such that the first three utterances are identical and
the fourth utterance is different. We then ask questions about goal and
emotion of the speaker for the fourth utterance. DIALeCT again generates
accurate inferences for the questions. The inferences also change
appropriately based on the distinct fourth utterances in the two dialogues.

Group | # |  Input |  Reference |  Generated Output
---|---|---|---|---
PO | (i) |  What is or could be the cause of target? <sep> target: Drive slowly, David. You could have an accident. <sep> context: $x$ |  David is driving very fast to flaunt his driving skills to the speaker. |  The speaker is warning david not to drive too fast.
(ii) |  For which utterance in the context the cause is the following: David is driving very fast to flaunt his driving skills to the speaker. <sep> context: $x$ |  Drive slowly, David. You could have an accident. |  You can count on me. I have been driving for years.
(iii) |  target: Drive slowly, David. You could have an accident. <sep> The cause of the target: David is driving very fast to flaunt his driving skills to the speaker. <sep> What is the subsequent event of the target? <sep> context: $x$ |  David ignores the speaker’s advice and continues driving with the same pace. |  The speaker warns david that if he drives too fast he will get into an accident.
SCAO | (i) |  What is or could be the cause of target? <sep> target: Drive slowly, David. You could have an accident. <sep> (0) David drives very slowly to flaunt his walking skills to the speaker. (1) David drives very slowly to flaunt his driving skills to the speaker. (2) David is driving very slowly to flaunt his driving skills to the speaker. (3) David is driving very fast to flaunt his driving skills to the speaker. (4) David walks very fast to flaunt his driving skills to the speaker. <sep> context: $x$ |  David is driving very fast to flaunt his driving skills to the speaker. |  David is driving very fast to flaunt his driving skills to the speaker.
(ii) |  answer: David is driving very fast to flaunt his driving skills to the speaker. <sep> target: Drive slowly, David. You could have an accident. <sep> context: $x$ |  cause |  subsequent event
(iii) |  The cause of the target: David is driving very fast to flaunt his driving skills to the speaker. <sep> target options: Drive slowly, David. You could have an accident. <utt> Look out! Red light! <utt> It doesn't matter. It is late. There is no one around. <utt> You can count on me. I have been driving for years. <sep> context: $x$ |  Drive slowly, David. You could have an accident. |  You can count on me. I have been driving for years.
CO | (i) |  For which utterance in the context the cause is related to the following concepts: drive, flaunt, driving, skill, speaker <sep> context: $x$ |  Drive slowly, David. You could have an accident. |  You can count on me. I have been driving for years.
(ii) |  For which utterance in the context the cause is the following: David is driving very fast to flaunt his driving skills to the speaker. <sep> concept: drive, accident <sep> context: $x$ |  Drive slowly, David. You could have an accident. |  Drive slowly, David. You could have an accident.
(iii) |  What is or could be the cause of target? <sep> concepts in the target: drive, accident <sep> context: $x$ |  David is driving very fast to flaunt his driving skills to the speaker. |  David was driving at a high speed.
(iv) |  What is or could be the cause of target? <sep> target: Drive slowly, David. You could have an accident. <sep> concepts in the answer: drive, flaunt, driving, skill, speaker <sep> context: $x$ |  David is driving very fast to flaunt his driving skills to the speaker. |  David was driving fast and flaunting his driving skills to the speaker.
DO | (i) |  target: Drive slowly, David. You could have an accident. <sep> corrupted concepts: drive, driving, flaunt, speaker <sep> context: $x$ <sep> concepts in the answer: |  drive, flaunt, driving, skill, speaker |  speaker, flaunt, driving, skill, drive
(ii) |  answer: David is driving very fast to flaunt his driving skills to the speaker. <sep> corrupted concepts: drive <sep> context: $x$ <sep> concepts in the target: |  drive, accident |  drive, accident
SO | (i) |  context: $x$ <sep> David is driving very fast to flaunt his driving skills to the speaker. <sep> A policeman caught david for breaking traffic rules. <sep> David was driving very fast and broked traffic rules. <sep> The speaker would tell the listener to apply brakes. <sep> David ignores the speaker’s advice and continues driving with the same pace. <sep> David is confident in his driving skills. <sep> The speaker is driving with overconfidence that leads him to miss the traffic signal. |  0 6 5 4 1 2 3 |  6 0 1 3 5 4 2
(ii) |  B: You can count on me. I have been driving for years. <utt> A: Look out! Red light! <utt> B: It doesn't matter. It is late. There is no one around. <utt> A: Don't let the police catch you. Oh, David, that's a policeman. He is waving over us. <utt> A: Drive slowly, David. You could have an accident. |  4 0 1 2 3 |  4 0 1 2 3

Table 13: An example of input, reference, and generated output triplets for
the various groups of objective functions (Section 4.2) from a dialogue
$\mathcal{D}$. PO, SCAO, CO, DO, and SO refers to the Primary Objectives,
Single Correct Answer Objectives, Concept-Based Objectives, Denoising
Objectives, and Sorting Based Objectives, respectively. The outputs are
generated from the pretrained DIALECT model. The context placeholder $x$ is
the concatenation of the utterances in the dialogue $\mathcal{D}$, which is
the following string: A: Drive slowly, David. You could have an accident.
<utt> B: You can count on me. I have been driving for years. <utt> A: Look
out! Red light! <utt> B: It doesn't matter. It is late. There is no one
around. <utt> A: Don't let the police catch you. Oh, David, that's a
policeman. He is waving over us.

Context |  Relation + Answers |  Label |  DIALeCT |  T5-Large
---|---|---|---|---
A: Wake up. It's almost eight o'clock. B: No, please. Let me sleep on! I couldn't get to sleep until 3 o'clock this morning. A: Why? What's wrong with you? B: I felt pain all over my body. Can you get me some medicine? A: Will aspirin do? B: No, aspirin isn't strong enough. A: Then I can do nothing but call for a doctor. |  Subseq: (0) The speaker would tell the listener to visit the doctor to get some better medicines. (1) The speaker would tell the listener to call the doctor who would prescribe them medicine. (2) The speaker would tell the listener to call the doctor to see if they could get some more medicine. (3) The speaker would tell the listener to visit the medical store nearby to get some better medicines. (4) The speaker would tell the listener to visit the emergency room to get some better medicines. |  0, 1, 3 |  0, 1, 3 |  0, 3, 4
A: Hello, Joan. Why are you late today? You are never late for work. B: No, I never. But … A: Wow! You coat's got very dirty! Did you fall? B: Yes, I had a terrible experience on the underground train. Listen to this! A man came up to me and pulled out a knife. He pointed it right at me! A: Oh, no! Are you all right? Did he hurt you? B: No, he didn't hurt me, but he took my handbag. A: Then what happened? What did you do? B: I caught hold of his knife, and he pushed me to the floor. A: Oh, no! Why did you catch hold of his knife? That's dangerous. B: I don't know. I didn't think. A: What did the other passengers do? Did they help you? B: Yes, they did. Two men ran after the robber and held him. A: Did the police come? B: Yeah. The conductor called a policeman, and he took the robber to the police station. A: Wow! What a story! Thank God you're all right. |  Subseq: (0) Joan would tell the listener that the thief asked him if he was okay. (1) Joan would tell the listener that the thief asked him to give him money and a watch. (2) Joan would tell the listener that the thief asked him to give him money and a cell phone. (3) Joan would tell the listener that the thief asked him to tell the police about the crime. (4) Joan told the listener that the thief asked him to hand over his keys. |  1, 2, 3 |  1, 2, 3 |  0, 2
A: Hello , may I help you ? B: Yes.We ' re interested in seeing the rooms for rent . A: Oh , how nice.They ' re bright rooms and the house is very quiet . B: A nice quiet house is exactly what we're looking for . A: Well , gentleman.Each room is $ 40 a week if you think that's OK . B: That sounds just wonderful to us . A: When do you want to move in ? B: How about this afternoon ? A: Fine . I'll be expecting you around two . |  Prereq: (0) The rooms showed to the person are currently unoccupied. (1) The rooms shown to the person are currently ready to be occupied. (2) The rooms they show are occupied. (3) The rooms showed to the person are full and occupied. (4) The rooms the person was looking in are currently occupied. |  0, 1 |  0, 1 |  1, 4
A: Paul, is that you? B: Yes, Mary. What can I do for you? A: Sorry to call you. But I just delivered my new computer. I am afraid I can't lift it by myself. Could you give me a hand to get it upstairs? B: Sure. Could you just give me a minute to finish off what I am doing? A: Yes, of course. But please hurry. The box is getting in the way. B: Don't worry. I'll be right down. |  Subseq: (0) Paul will get down to pick up the computer from mary. (1) Paul will get downstairs to help mary in lifting the computer upstairs. (2) Paul will get upstairs to help mary in lifting the box upstairs. (3) Paul will get downstairs to help mary in lifting the box upstairs. (4) Mary will help paul lift the computer. |  1, 3 |  1, 2, 3 |  1, 3

Table 14: Examples of the fine-tuning performance of DIALeCT and its
comparison with T5-Large.