# Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal
Summarization and Text-Units-Text Generation

###### Abstract

Health coaching helps patients achieve personalized and lifestyle-related
goals, effectively managing chronic conditions and alleviating mental health
issues. It is particularly beneficial, however cost-prohibitive, for low-
socioeconomic status populations due to its highly personalized and labor-
intensive nature. In this paper, we propose a neuro-symbolic goal summarizer
to support health coaches in keeping track of the goals and a text-units-text
dialogue generation model that converses with patients and helps them create
and accomplish specific goals for physical activities. Our models outperform
previous state-of-the-art while eliminating the need for predefined schema and
corresponding annotation. We also propose a new health coaching dataset
extending previous work and a metric to measure the unconventionality of the
patient’s response based on data difficulty, facilitating potential coach
alerts during deployment.

Keywords: Dialogue Systems, Neuro-Symbolic AI, NLP in Healthcare

Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal
Summarization and Text-Units-Text Generation

Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp,
---
Bing Liu and Nikolaos Agadakos
University of Illinois Chicago, Illinois, United States
{yzhou232, bdieugen, bziebart, sharpl, liub<EMAIL_ADDRESS>

Abstract content

## 1\. Introduction

Health coaching is a patient-centered clinical practice that aims to help
patients achieve personalized and lifestyle-related goals to enhance their
health behaviors. It has demonstrated efficacy in managing chronic conditions
like diabetes and cardiovascular disease, as well as alleviating mental health
issues such as anxiety and depression (Butterworth et al., 2006; Ghorob, 2013;
Kivelä et al., 2014; Thom et al., 2016). Health coaching is particularly
advantageous for low-socioeconomic status (SES) populations, who endure a
disproportionate burden of physical and mental health issues (Thackeray et
al., 2004; Kangovi et al., 2014). Nonetheless, it is invariably cost-
prohibitive for these populations due to its highly personalized and labor-
intensive nature; additionally, it also requires considerable time commitment
since it normally spans several sessions across weeks or even months.

Recently, efforts have been undertaken to enhance the effectiveness of health
coaching through natural language processing techniques. Our group (Gupta et
al., 2020a, b, 2021) collected real-world datasets of health coaching
conversations and proposed a goal summarization approach based on slot-filling
and dialogue state tracking. We further improved goal summarization and
introduced an efficient health coach dialogue system that supports empathetic
response generation (Zhou et al., 2022). An excerpt of a health coaching
conversation is illustrated in Table 1.

Excerpt of Patient-Coach Conversation # 17 Week # 7 Coach: Hi [Name]! I hope
you had a great weekend :) You are now in the second to last week of the
study! Coach: Your goal last week was to reach 7000 steps everyday - you got
close! Coach: Did you want to keep your walking goal the same this week or
lower it a bit? Patient: You can lower it to 5000 Coach: Okay, sounds good -
and what days are you thinking to do 5,000 steps? Patient: 7 days Coach:
awesome - and what’s your confidence on a scale from 1-10 that you can reach
this goal? … … … Coach: Hi [Name] - you’re doing a really great job with your
goal this week. Coach: You got over 11,000 steps on Monday and yesterday -
that’s over double your goal! Amazing job :) Patient: Thanks and you too
Coach: Hi [Name] - Great job reaching your goal last week. You got over 5,000
steps all 7 days! You should be really proud of yourself :) Excerpt of
Patient-Coach Conversation # 17 Week # 8 Coach: This is your last week in the
study - what would you like your final walking goal to be? Patient: Stay the
same as last week. … … …

Table 1: An excerpt from a health coaching conversation from our dataset. Each
week the coach and patient collaboratively establish a feasible goal. Then,
the coach monitors the patient’s progress, maintains the patient’s engagement,
and addresses the patient’s concerns. The coach may discuss the goal by
referring to the goal settled before.

However, these works are based on simplified dialogue states (a set of goal
attribute-value pairs) and require a designed schema with corresponding human
annotations, which are not only labor-intensive but also lose the global
contextual information that is established over several sessions (in our case,
from 4 to 8 weeks). In addition, the considerable length of each sample,
together with the limited data size, poses significant challenges in
healthcare dialogue modeling. To address these issues, we propose a neuro-
symbolic goal summarization approach that eliminates the need for a pre-
designed schema or corresponding annotations yet preserves interpretability.
The model is optimized to (i) summarize the goal as effectively as possible,
given the current week’s dialogue, and (ii) generate an executable instruction
on how to modify the summarized goal by referencing the goal from the previous
week(s). We also introduce a text-units-text dialogue generation approach that
considers as input a sequence of discrete units symbolizing the dialogue
history. Moreover, we propose a more generalized approach to detect patient’s
unconventional responses by extending Point-wise $\mathcal{V}$-usable
Information (PVI) Ethayarajh et al. (2022) for dialogue generation, without
using any external datasets, contrasting with our previous work (Zhou et al.,
2022). Finally, we introduce a novel health coaching dataset consisting of
1880 dialogue turns from 22 patient-coach conversations, each spanning up to 8
weeks. The dataset is enriched with Fitbit data, which tracks the progress of
patients’ goals. We aim to augment the existing health coaching datasets and
provide a more robust testing benchmark for health coaching modeling,
particularly in potential domain-shift scenarios.

We evaluate our model by both automatic metrics and expert-based human
evaluation. Experimental results show that our neuro-symbolic goal summarizer
outperforms the current state-of-the-art by up to $\sim$30% in semantic frame
accuracy. In addition, our text-unit-text dialogue generation achieved the
best performance compared to previous work in all metrics for all datasets.
Health coaches prefer our generated responses over the previous state-of-the-
art 33.9% of the time, while the vice versa happens 19.6% (the rest are ties).

Our contributions are (1) a data-efficient neuro-symbolic goal summarization
model and text-units-text dialogue generation model for health coaching, which
outperform previous state-of-the-art while eliminating the need for predefined
schema and corresponding annotation; (2) a metric measuring the
unconventionality of the patient’s response in terms of data difficulty,
facilitating potential coach alert during deployment and data characterization
during training; and (3) a novel health coaching dataset.

## 2\. Related Work

##### Conversational Agents in Healthcare.

Conversational agents have been employed in healthcare to enhance the
efficiency and scalability of interactions between healthcare professionals
and patients. For instance, chatbots have been utilized in various healthcare
contexts, such as chronic disease monitoring (Chaix et al., 2020), cognitive
behavior therapy (Fitzpatrick et al., 2017), and physical activity promotion
(Mohan et al., 2020; Kocielnik et al., 2018). Nevertheless, these systems
often exhibit limitations in their natural language understanding and
generation capabilities. More advanced approaches have been proposed for
mental health counseling (Althoff et al., 2016; Shen et al., 2020) and health
coaching. Our group (Gupta et al., 2020a, b, 2021) collected two real-world
health coaching conversation datasets, focusing on the NLU components that
summarize weekly goals to support health coaches. Building upon these
datasets, we developed a data-efficient health coaching dialogue system with a
simplified NLU and NLG framework and mechanism-conditioned empathetic response
generation (Zhou et al., 2022). In recent years, there has been a shift in
domain-specific dialogue systems from modularized and NLU-NLG component-based
designs (Jokinen and McTear, 2009; Williams et al., 2016; Budzianowski et al.,
2018; Mrkšić et al., 2017; Wen et al., 2015) towards end-to-end architectures
to reduce human effort and error propagation between modules (Hosseini-Asl et
al., 2020; Peng et al., 2021).

##### Neuro-symbolic Approaches

have recently gained significant attention due to their ability to facilitate
end-to-end while leveraging symbols for interpretability and data-efficient
training. Mao et al. (2019) proposed a neuro-symbolic concept learner that
combines the strengths of both neural networks and symbolic logic,
demonstrating improved performance in visual question-answering tasks. Lamb et
al. (2019) introduced the Neuro-Symbolic Transformer, which leverages symbolic
reasoning within a transformer-based architecture in text classification
tasks. De Raedt et al. (2019) proposed a neuro-symbolic approach that
demonstrates the potential of neuro-symbolic systems in handling complex
reasoning tasks. Another work in this area is the exploration of end-to-end
differentiable natural logic modeling Feng et al. (2020). Dong and Lapata
(2018) proposed a coarse-to-fine decoding method for neural semantic parsing,
utilizing a structure-aware neural architecture. Arabshahi et al. (2021)
explores a neuro-symbolic approach for enhancing conversational dialogue
systems with commonsense reasoning capabilities. Garcez et al. (2018) provides
a comprehensive survey and taxonomy of approaches that combine neural and
symbolic methods for learning and reasoning in AI systems.

## 3\. Health Coaching Datasets

##### Background

A health coaching process often starts with a goal-setting stage where the
coach discusses creating a S.M.A.R.T. goal with the patient, namely a goal
that is specific, measurable, achievable, relevant, and time-bound Doran
(1981). Once the goal is settled, the coach will monitor the patient’s
progress and maintain patient engagement. Our research group (Gupta et al.,
2020a) collected two datasets (dataset 1 and 2) of health coaching dialogues
between patients and coaches via text messages. Previously, we defined ten
slots for the goal’s attributes (types of activity, amount, time, days,
location, duration, and the confidence score for the activity). We also used a
stage-phase schema for additional turn-level annotation and added dialogue act
annotations in our later work (Gupta et al., 2021).

##### New Human Data Collection

Emulating their settings, we recruited four health coaches trained in SMART
goal setting and a cohort of 22 patients. The study was conducted over eight
weeks. The communication between the coach and the patient was facilitated
through text messaging applications, and Fitbits were provided to monitor
patients’ activity progress. However, six patients withdrew in the early weeks
of the study. Consequently, the dataset comprised 1880 dialogue turns over 102
weeks. The datetime of messages, interlocutors, dialogue content, Fitbit
action data, and patient and health coach IDs are available for each dialogue.
The data were thoroughly anonymized. In contrast to the first two rounds of
data, we retained the emoji tokens in the dataset for future analysis. Data
statistics are shown in 2. A sample of the health coaching conversation
between the patient and health coach is presented in Table 1. The full dataset
is available at https://github.com/uic-nlp-lab/virtualcoachdata/.

## 4\. Methods

#P/C #W #T #T/(W,P) Dataset 1 27/1 4 2,853 26.6 Dataset 2 28/3 5-8 4,134 18.8
Ours 22/4 2-8 1,880 18.4

Table 2: Dataset Statistics. #P/C, #W, #T, #T/(W,P) refer to the number of
patients/health coaches involved, study weeks, total dialogue turns, average
turns per patient per week.

This section presents the two integral components of our health coaching
dialogue system: the Neuro-Symbolic Goal Summarizer and the Neuro-Symbolic
Dialogue Generator.

### 4.1. Neuro-Symbolic Goal Summarizer

The task of goal summarization involves processing the dialogue history
between the health coach and the patient to generate the negotiated goal in
natural language. The dialogues in health coaching often span multiple weeks.
When the coach discusses the current week’s goal, they may refer back to the
attributes of the goal implemented in the previous week. However,
incorporating all text from previous weeks can result in excessively long
inputs, which, given the limited dialogue samples, can be challenging to train
on. Our previous research (Zhou et al., 2022; Gupta et al., 2021) approached
this task by formulating it as dialogue state tracking, where the state is
defined as a set of goal attribute-value pairs. However, these methods require
a pre-defined schema with corresponding annotations and a separate slot-
filling module, which do not optimally utilize the entire context, resulting
in compromised information.

To address these challenges, we aim to provide a data-efficient and end-to-end
yet interpretable solution and propose a neuro-symbolic approach for goal
summarization. The approach considers the dialogue of the current week while
referencing the previous week’s goal. In addition, it does not require a
schema or corresponding annotations. We aim to optimize the model to summarize
the goal, focusing on two key aspects: (i) summarizing the goal as effectively
and comprehensively as possible, given the _current_ dialogue, and (ii)
generating a feasible instruction on how to modify the summarized goal from
(i) based on the previous goal. As an example depicted in Figure 1, the coach
elaborates on the goal for week $w_{t}$ by referencing the goal established in
the preceding week $w_{t-1}$ (_“same days?”_). Our model learns to generate an
executable instruction (Copy {Days}) and extract the partial goal (_“Walk
2,500 steps”_) from the current week’s dialogue, and execute the instruction
on the referenced previous week’s goal to generate the complete goal
summarization: _“Walk 2,500 steps from Monday to Friday.”_

To achieve that, we formulate the problem within the context of Reinforcement
Learning (RL), specifically utilizing the Proximal Policy Optimization (PPO)
(Schulman et al., 2017) framework. We maximize the following training
objective:

Figure 1: A simplified demonstration of Neuro-Symbolic Goal Summarization. The
health coach discusses the goal for week $w_{t}$ by referring to the goal set
in the previous week $w_{t-1}$ (_"same days?"_). Our model is trained to
generate an executable instruction (Copy {Days}) and to extract the partial
goal (_"Walk 2,500 steps"_) from the dialogue of the _current_ week. The model
then edits the partial goal by applying the instruction to the reference
previous goal, resulting in the comprehensive goal summary: _"Walk 2,500 steps
from Monday to Friday."_

$\text{objective}(\phi)=\mathbb{E}(x,y)\sim
D_{\pi_{\phi}^{RL}}[r(y,y^{*}|x)\\\ -\lambda\text{
KL}(\pi_{\phi}(y|x)||\pi_{base}(y|x))]$ (1)

where $\pi_{\phi}$ is the learned RL policy (e.g., the language model to be
fine-tuned) and $\pi_{base}$ is the untouched pre-trained language model. $r$
is the reward function (we use ROUGE score (Lin, 2004)), and the KL term is
the Kullback–Leibler Divergence as regularization. We aim to optimize the
likelihood of the generated sequences, comprising partial goals and
instructions, that Instruction{Partial Goal, Reference} subsequently results
in ground truth summarization. The full set of instructions (e.g., Add {Num},
Copy {Times}) will be available in the Appendix.

### 4.2. Text-Units-Text Dialogue Generator

#### 4.2.1. Generation with Unit Symbols

A prevalent issue in healthcare dialogue datasets is the extensive length of
each sample, associated with the limited number of samples. Training a
sequence-to-sequence generation model using a long context window presents a
significant challenge. However, an interesting observation is that healthcare
dialogues typically follow a similar pattern. For instance, health coaching
invariably begins with the coach discussing a realistic goal with the patient,
confirming each aspect of the goal, such as activity, amount, day, and times.
Once the goal is established, the coach monitors the patient’s progress and
sustains patient engagement.

Inspired by this observation and the recent success of speech-to-unit modules
that predict the discrete representations of the speech (Lee et al., 2022;
Lakhotia et al., 2021), we explore the possibility of utilizing a short
context window while preserving the dialogue history information through
symbolic abstraction in text-to-text dialogue generation. Specifically, our
response generation incorporates the following two models:

##### Dialogue-History-to-Unit Encoder

encodes the long dialogue history into discrete unit symbols using out-of-box
pre-trained language models and unsupervised approaches. We first encode each
turn of the conversation in the training dialogues with SBERT, then run
k-means clustering to obtain K clusters; we subsequently designate the cluster
indices as units. Despite the unique details of each dialogue, we anticipate a
similar sequence of units to emerge as each dialogue progresses. Furthermore,
the total number of units in a dialogue can indicate the dialogue’s
progression length.

##### Units-to-Text Generation

We propose a sequence-to-sequence model that generates responses for a virtual
coach. The model takes as inputs a sequence of discrete units
$N_{1},...,N_{t-1}$ symbolizing the dialogue history, the most recent dialogue
turns from the coach $C_{t-1}$ and the patient $U_{t-1}$, and a partial goal
$G_{t-1}$ summarized as discussed in Section (4.1) based on the current
dialogue. All inputs are concatenated as a single sequence. Then the generated
virtual coach response $R_{t}$ is defined as:

$R_{t}=\textrm{Seq2Seq}([N_{1},...,N_{t-1},G_{t-1},R_{t-1},U_{t-1}])$ (2)

#### 4.2.2. Measuring Data Difficulty of User Responses

Identifying unconventional user responses is vital in domain-specific dialogue
systems to circumvent system failures due to unsupported user inputs. It can
be particularly critical in healthcare settings, where patients often express
concerns and emotions that exceed the system’s capabilities or do not respond
directly to the system’s prompts. The unconventional inputs from patients,
coupled with the limited availability of healthcare training data, can pose
significant challenges to the model’s training process. In this work, we show
the potential of leveraging data difficulty to identify unusual patient
inputs.

Data difficulty metrics categorize data into easy-to-learn and difficult-to-
learn examples based on training dynamics (Pleiss et al., 2020; Swayamdipta et
al., 2020) or mutual information Xu et al. (2020); Ethayarajh et al. (2022).
Previous research primarily focuses on classification tasks, where a
difficult-to-learn example typically signifies that the example is mislabeled.
Xu et al. (2020) propose a framework called predictive
$\mathcal{V}$-information to measure how much information can be extracted or
learned from input $X$ about its label $Y$ when constrained to functions or a
model family $\mathcal{V}$. The predictive $\mathcal{V}$-information,
$I_{\mathcal{V}}(X\\!\rightarrow\\!Y)$ is defined as the difference between
predictive $\mathcal{V}$-entropy $H_{\mathcal{V}}(Y)$ and conditional
$\mathcal{V}$-entropy $H_{\mathcal{V}}(Y|X)$:

$I_{\mathcal{V}}(X\\!\rightarrow\\!Y)=H_{\mathcal{V}}(Y)-H_{\mathcal{V}}(Y|X)$
(3)

The greater the $I_{\mathcal{V}}(X\\!\rightarrow\\!Y)$, the easier the dataset
is for $\mathcal{V}$. Extending from Xu et al. (2020), Ethayarajh et al.
(2022) propose Point-wise $\mathcal{V}$-usable Information (PVI) to measure
the information in the _individual_ instance usable by a model family
$\mathcal{V}$ with respect to the data distribution. The PVI of an instance
$(x,y)$ is defined as:

$\operatorname{\mathrm{PVI}}(x\\!\rightarrow\\!y)=-\log_{2}g^{\prime}_{y}(\varnothing)+\log_{2}g_{y}(x)$
(4)

where $g^{\prime}\in\mathcal{V}$ s.t. $\mathbb{E}[-\log
g^{\prime}_{Y}(\varnothing)]=H_{\mathcal{V}}(Y)$ and $g\in\mathcal{V}$ s.t.
$\mathbb{E}\left[-\log g_{Y}(X)\right]=H_{\mathcal{V}}(Y|X)$. If $\mathcal{V}$
were the BERT function family, $g$ and $g^{\prime}$ would be the models after
finetuning BERT with and without the input, respectively. An instance with a
large negative PVI value is considered as “difficult,” showing that the model
can better predict the majority class when ignoring $X$, which often indicates
the instance is mislabeled.

Inspired by their work, we aim to explore the difficulty in predicting user
responses given the most recent dialogue context, which will provide insights
into the degree of unconventionality or surprise in the user’s response and
its divergence from the dataset distribution. This approach could additionally
serve as an easy-to-implement and general out-of-domain detection, eliminating
the need to construct a separate domain-specific detection classifier (e.g.,
empathy detector (Zhou et al., 2022)). To achieve this, we propose an
_extension to the PVI_ in sequence-to-sequence _generation_ as:

$\operatorname{\mathrm{PVI}}(x\\!\rightarrow\\!y)=-\underbrace{\sum_{t=1}^{n}log(p(y_{t}|y_{<t},\varnothing))}_{\text{by
}g{}^{\prime}}\\\ +\underbrace{\sum_{t=1}^{n}log(p(y_{t}|y_{<t},x))}_{\text{by
}g}$ (5)

where $x$ and $y$ are the sequence of the dialogue context and the user
response, and $g$ and $g^{\prime}$ are the generative models after fine-tuning
with and without the dialogue context, respectively. An instance (user
response) with a large negative PVI value is considered “difficult,” showing
that the model can better predict the user response when ignoring the context,
indicating the unconventionally in the user response.

The difficulty metric we propose serves two functions: Firstly, it assists in
identifying unconventional patient responses during deployment. If such a
response were detected, the system could alert the human coach, indicating
potential patient concerns that require attention or suggesting that the
patient’s utterance or question may not be within the system’s capabilities.
Secondly, the difficulty metric acts as a data filter during training. It
categorizes our limited data into subsets of easy-to-learn and hard-to-learn
instances, facilitating possible curriculum learning or denoising approaches
for our system.

## 5\. Experiments

### 5.1. Experimental Settings

To ensure comparability, we use dataset 1 and dataset 2 from Gupta et al.
(2021) for training/development and testing, following our previous work (Zhou
et al., 2022). We also employ identical model architecture backbones as
before, specifically utilizing T5 Raffel et al. (2020) for the goal
summarization task and GPT-2 Radford et al. (2019) for the response generation
task. We additionally evaluate model response generation performance on our
newly collected dataset 3 for generalizability in possible domain-shifting. To
mitigate inefficient sampling, we manually annotate 40 positive examples. We
also use two T5-base models for our PVI-generation metrics. We choose $k$ = 15
for deriving the discrete units. Further details and model parameters will be
available in the Appendix.

##### Evaluation Metrics

We use semantic frame correctness for evaluating the goal summarization, which
is identical to goal correctness@$k$ with $k$ = 10, used by our previous work
Gupta et al. (2021); Zhou et al. (2022). For dialogue response generation, we
use BLEU (Papineni et al., 2002), BertScore (Zhang et al., 2020), and
Perplexity (PPL). We measure fluency as perplexity (PPL) of the generated
response using a pre-trained GPT2 model that has not been fine-tuned for this
task, following previous work (Ma et al., 2020; Sharma et al., 2021).

.

### 5.2. Results

We compare our Neuro-symbolic Summarization model with the best performance
reported by Gupta et al. (2021) and Zhou et al. (2022) in Table 3. D1 (F), D1
(B), and D2 refer to dataset 1 (Forward), dataset 1 (backward), and dataset 2.
Forward and Backward refer to the two points where they labeled the goals for
each week: one at the end of the goal-setting stage (forward) and the other at
the end of the goal-implementation stage (backward). Our approach improves
semantic frame correctness by a significant margin ($\sim$ 20$\%$ \-
$\sim$30$\%$), compared with previous work that utilizes the traditional slot-
filling and state-tracking framework.

Model D1 (F) D1 (B) D2 Gupta et al. (2021) 15 13.1 - Zhou et al. (2022) 21.7
31.6 13.6 Ours 52.3 44.8 46.7

Table 3: Goal summarization performance by semantic frame correctness. D1 (F),
D1 (B), and D2 refer to dataset 1 (Forward), dataset 1 (backward), and dataset
2. Our model outperforms previous work by a significant margin in all three
datasets.

Table 4 shows the model performance on dialogue response generation. We employ
two settings to train our response generation model: (1) Original. Using the
original data, and (2) Low-PVI-Replace. substituting the patient’s response,
which has been assigned a large negative PVI value, with an alternative
response from a similar context with a positive PVI in the training set. This
context is identified by locating the semantically closest previous coach
utterance using sentence-BERT embeddings. We substitute 5% of the patient’s
utterances, prioritizing those with the most negative PVI values in the
training set. We acknowledge that patient utterances with low negative PVI
values are not necessarily "noise," and such substitutions could potentially
disrupt the coherence of subsequent dialogues. However, we have found that
this replacement strategy generally aids our model’s convergence, particularly
with limited training data. Our model (both with and without PVI fix)
outperforms previous work on both dataset 2 and our newly collected dataset in
all metrics. We have identified a domain shift in our collected dataset,
characterized by patients exhibiting less verbal communication and increased
usage of emojis. Coaches have adapted to maintain the continuity of the
dialogue by becoming more talkative. The domain shift negatively impacted our
model’s performance.

Data 2 Data 3 Model BLEU ($\uparrow$) PPL ($\downarrow$) BertS F1 ($\uparrow$)
BLEU ($\uparrow$) PPL ($\downarrow$) BertS F1 ($\uparrow$) Zhou et al. (2022)
25.1 15.6 87.2 20.6 19.7 83.5 OURS+Original 26.7 14.7 88 22.5 16.7 84.4
OURS+Low-PVI-Replace 28.4 14.1 89.2 23.7 15.3 84.1

Table 4: Evaluation on dialogue generation. BLEU: Average of BLEU-1,-2,-3,-4.
BertS F1: Averaged BERT-score F1. Our model (both with and without PVI fix)
outperforms previous work on both dataset 2 and our newly collected dataset 3
in all metrics. Nevertheless, the performance of all models diminishes on
dataset 3 due to possible distribution shifts.

##### Human Evaluation

We conducted an expert-led assessment of the generated coach responses via A/B
testing. Four health coaches were consulted to evaluate our model’s outputs
compared to those from previous state-of-the-art Zhou et al. (2022), given the
same dialogue histories. The coaches were asked to select one of the following
options: (a) Response A is most suitable in the given context; (b) Response B
is most suitable in the given context; (c) Tie; both responses are equally
good, or (d) Neither is appropriate. Among 56 collected samples, our model’s
outputs have a 14.29$\%$ preference over previous state-of-the-art Zhou et al.
(2022) (as the difference between the preferences for one or the other).
However, approximately 30$\%$ of the generated responses were not deemed
appropriate by our experts. This indicates the ongoing challenge of generating
high-quality health coaching responses, particularly given the constraints of
limited data and the sensitive nature of health-related interactions. See
Figure 2.

Figure 2: Human evaluation of generated response by health coaches. Our
model’s outputs have a $14.29\%$ preference over previous state-of-the-art
Zhou et al. (2022).

### 5.3. Qualitative Analysis

We present the output examples from our neuro-symbolic goal summarizer and
dialogue generator, comparing them to previous work. We also demonstrate
generation from GPT-3.5-turbo111https://platform.openai.com/docs/guides/chat
(two-shot in-context exemplars due to the input context limit), highlighting
the limitation of utilizing LLMs in low-resource healthcare settings.

Table 5 compares goal summarization results from various models given the same
dialogue history example. The dialogue example contains misleading
information, such as _“Friday”_ and _"2 miles a day"_ (highlighted in red in
the Table), while the part of the ground-truth information is in the previous
goal (_"7 days a week"_), as indicated by the bolded text _"same as last
week"_ that coach and patient agree to. Our previous methodologies, which rely
on slot-filling and update dialogue states as the goal by either the last
appearance of the slot-value Gupta et al. (2021) or a carryover classification
Zhou et al. (2022) and neglect the global context, fail to extract the correct
goal information. In contrast, not only does our model summarize _"3 miles"_
from the dialogue but also generates the instruction of copy(days), applying
this instruction to the previous goal to complete the summarization with _"7
days a week."_ Moreover, our model delivers outputs in a natural language
format, which is more user-friendly and comprehensible, particularly for
health coaches, compared to the slot-values format. Lastly, the result from
GPT3.5-turbo only captures partial information but asserts that the goal is a
well-defined SMART goal.

Dialogue + [Previous Goal: walk 2 miles a day 7 days a week.] Coach: Also,
what would you like to set as your SMART goal this week? Patient: Hey bro last
Friday I met my goal but I didn’t have the Fitbit on. Patient: On Tuesday I
had it on but it went dead. I met my goal on that day also. My goal for this
week is 2 miles a day. Coach: No problem, making the goal is most important.
Your body is your tracker. Patient: Ok I’m excited and thanks for your help.
Coach: Okay so you want to stick with your same goal as last week? FYI 2 miles
is 4000 steps. Patient: Let’s make it 3 miles. Coach: Base on your steps the
first week I think 3 miles a day is doable if we monitor your step and have a
plan… Coach: How many days do you want to go for? Patient: Same as last week?
Coach: Sure. And last what might get in the way of you accomplishing your
goal? Patient: Nothing. Patient: I’m ready. Coach: Sounds like a plan. Good
luck!! Model Predictions Gupta et al. (2021): {Activity:Steps, Amount:[3
miles, 4000 steps], daytimes:Tuesday} Zhou et al. (2022): {Activity:Walk,
Amount:3 miles, daytimes:Friday} Ours: Walk 3 miles a day 7 days a week.
Instruction:Copy{Days} GPT-3.5-turbo: "Goal: 3 miles a day." - This goal is
Specific (3 miles), Measurable (can be tracked by steps or distance),
Achievable (based on the patient’s previous performance), Relevant (to the
patient’s fitness objectives), and Time-bound (for the duration of the week).
It’s a well-defined and attainable goal.

Table 5: Qualitative examples of summarized goal from different models.

As illustrated in Table 6, our Extended Point-wise $\mathcal{V}$-usable
Information (PVI) for Generation measures unconventionality in the patient’s
response given the dialogue context. A significantly negative value indicates
that the model struggles to anticipate the response, implying a higher degree
of surprise in the patient’s response. Conversely, a positive value suggests a
more predictable and conventional response. Responses that either fail to
directly address the coach’s question (example #1-3) or fall outside the
domain of the conversation (example #4, the patient intended to show her dog
named King watching Youtube) are assigned a negative value. On the other hand,
if the patient’s response closely aligns with the coach’s utterance, PVI will
be positive. Interestingly, the response “short of breath” is unconventional
while aligning with the coach’s question, resulting in a moderately low value.

Table 7 shows response generation outputs from different models. In Dialogue
#1, the model by Zhou et al. (2022) failed to generate a coherent response due
to the model’s conditioning on the state of goal slot values while
disregarding the critical contextual information (“set the same goals”).
Interestingly, GPT-3.5 recognizes the patient’s adherence to their goals but
still prompts for a new goal definition. However, our model generates a
coherent response that benefits from accurate Neuro-symbolic goal
summarization. In Dialogue #2, Our previous model failed to generate an
appropriate response due to an error in updating the goal stage. The model
incorrectly predicts the dialogue to be in the goal-setting stage, hence
attempting to fill the empty attribute of the goal (e.g., days). In contrast,
our NS generation does not utilize or predict stage information. Instead, it
infers the dialogue progression, which is more likely to be in the goal
implementation stage, based on a sequence of discrete units. It then focuses
on the patient’s concerns rather than completing the goals. Surprisingly,
GPT3.5 failed to respond to the patient’s concern, even when explicitly
indicated in the prompt. The complete prompt for GPT3.5 dialogue generation
can be found in the appendix.

Dialogue Context$\,\to\,$Patient Response PVI … Coach: and one last thing, how
would you rate the goal? $\,\to\,$ Patient: yes and yes. -0.11 … Coach: What
would you like to set as your SMART goal this week? $\,\to\,$ Patient: Bam!
Did you see the weekend? -0.259 … Coach: Great and what days it will be?
$\,\to\,$ Patient: Yes, but we can move it up to 8,000 steps. -0.391 … Coach:
It’s also important to be kind to yourself. $\,\to\,$ Patient: This is King.
Looking at Youtube. -0.57 … Coach: Great would you be available at 9am or
11am? $\,\to\,$ Patient: 11am 1.875 … Coach: How confident are you that you
can reach your goal on a scale from 1 - 10? $\,\to\,$ Patient: 10. Absolutely.
1.498 … Coach: what days would you like to do the elliptical? $\,\to\,$
Patient: Wednesday, Friday, and Saturday. 1.096 … Coach: oh no! I’m sorry to
hear that. What happened? $\,\to\,$ Patient: Short of breath. 0.78

Table 6: Our Extended Point-wise $\mathcal{V}$-usable Information (PVI) for
Generation measures unpredictability in the patient’s response given the
dialogue context. A large negative value shows the model’s difficulty in
predicting the response, indicating a higher degree of surprise in the
patient’s response. On the other hand, a positive value indicates a more
predictable and conventional response.

Dialogue #1 Coach: Hi [Name], hope you’re well. I forgot to message you
yesterday to set your goal for Week 6, my apologies! Coach: You met your goal
5 out of 7 days last week, great job What would you like to do for this week?
Patient: Gd morning [Name], I am going to set the same goals. With a 7
confidence. Model Response Zhou et al. (2022): That’s great. And think about
what activity would you like to set for your goal? Walks, stairs, etc Ours:
Ok, great! Can you think of any barriers that might prevent your goal?
GPT-3.5-turbo: That’s fantastic to hear that you’re sticking to your goals!
Let’s make sure it’s a SMART goal for Week 6. Can you provide more details
about the goal, like the specific number of steps or any changes in frequency,
time, or location? Dialogue #2 Coach: Hi [Name]! Hope you had a good weekend
and were able to stay cool/dry! How are you feeling about your 5,000 steps
goal this week? Patient: Good morning. I’m feeling good about it. I will start
today (Monday) this weekend I had a party on Saturday and rested on Sunday.
Coach: Sounds like a fun weekend! I’m excited to see your steps for today and
the rest of the week! Patient: Loved "Sounds like a fun weekend! Iḿ excited to
see your steps for today and the rest of the week!" Coach: You are totally
crushing your goal this week! Great job! Keep it up! Patient: Thank you.
Coach: Reminder to sync your Fitbit! Patient: What do you mean? Are you unable
to track me? Model Response Zhou et al. (2022): What days? Ours: Oh, Just
wanted to remind you. GPT-3.5-turbo: That’s great to hear you’re feeling good
about your goal! How many days this week do you plan to aim for those 5,000
steps?

Table 7: Qualitative examples and error analysis for response generation.

## 6\. Conclusion

We propose a neuro-symbolic goal summarization approach that (i) summarizes
the goal as effectively as possible, given the current week’s dialogue, and
(ii) generates an executable instruction on modifying the summarized goal by
referencing the previous goal. We also build a text-units-text dialogue
generation approach that considers as input a sequence of discrete units
symbolizing the dialogue history. Our models outperform previous state-of-the-
art while eliminating the need for a pre-designed schema or corresponding
annotations. This significantly minimizes the annotation labor cost and
development cycle, benefiting healthcare and other resource-constrained
scenarios. The framework also exhibits the potential for generalization to
other applications, including developing dialogue systems in patient education
upon discharge or consultations regarding behavioral change issues. However,
there remains to be a disparity between the responses generated by the model
and those of expert human levels. Given the nature of data limits and
sensitivity in healthcare, generating human-level, fair, and faithful outputs
while ensuring the model’s interpretability is a significant and challenging
direction for future exploration.

## 7\. Acknowledgments

The work is supported by the National Science Foundation under Grant
IIS-1838770.

## 8\. Bibliographical References

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##

  * (1)

##  Appendix A Instruction Set

The full set of instruction on goal editing based on the reference is shown in
Table 8.

Instruction Explanation " " Pass Copy {Times} Copy times, e.g., “after 8 pm”
Copy {Days} Copy days, e.g., “Mon-Fri” Copy {Num} Copy number, e.g., “3000
steps” Add {Num} Add number from previous goal. Add {Days} Add days from
previous goal. Copy {All} Copy all info from previous.

Table 8: The executable instructions we used in the neuro-symbolic goal
summarization.

##  Appendix B Prompts

We show the templates we use when prompting the language model for goal
summarization and response generation.

##### Prompt for Goal Summarization:

Summarize the SMART goal discussed in the health coaching dialogue. The goal
attributes include details such as the activity (e.g., walking), the quantity
of the activity (e.g., 4000 steps), the schedule (e.g., Monday-Friday, after 4
pm), locations, and more:/n

##### Prompt for Response Generation:

As a health coach, your task is to refine the patient’s goal into a SMART
goal. Ask for details like frequency, time, duration, location, and confidence
level, but only one aspect at a time. After establishing the goal, monitor the
patient’s progress and keep them engaged. Always address their concerns
concisely. Now, consider the following dialogue context and formulate your
response:/n

##  Appendix C Quantitative Results of LLMs

Due to resource constraints, we run the goal summarization and dialogue
generation on 100 randomly selected examples with GPT-3.5-turbo. We show the
results in Table 9.

Response Generation PPL 23.6 BLEU 14.21 BertScore 85.62 Goal Summarization
Semantic Frame Acc 41.0

Table 9: Performance of GPT-3.5-turbo on sampled response generation and goal
summarization data.

##  Appendix D Training Details

All the following models use Huggingface Transformers Library (Wolf et al.,
2020). The hyperparameters are not extensively fine-tuned.

For the goal summarizer, we use T5-base as the model backbone. To mitigate
inefficient sampling, we manually annotate 40 positive examples, i.e., the
sequences of instructions and partial goals that result in ground truth. The
model was first fine-tuned with dialogue-to-goal pairs and then with the 40
examples we labeled. Finally, we contrast the sampled negative examples (the
ones that fail) with the positive examples to update the gradient.

We also use two T5-base models to train our PVI-generation metrics. Model $g$
was trained with the context mapping to the patient response for two epochs,
while $g^{\prime}$ was trained with an empty context with one epoch.

For dialogue generation, we choose $k$ = 15 for deriving the discrete units.
We use GPT-2-large as the model backbone with a max sequence length set to 128
since the dialogue history has been symbolized as discrete units. The model
was trained for 7.0 epochs with a learning rate of 1e-4, with a batch size of
16. We use sampling during decoding with top-k set to 40 and top-p set to 1.