PsyChat: A Client-Centric Dialogue System for
Mental Health Support
Huachuan Qiu1,2 , Anqi Li1,2 , Lizhi Ma2 , Zhenzhong Lan2,†
1

arXiv:2312.04262v1 [cs.CL] 7 Dec 2023

2

Zhejiang University, Hangzhou, China
School of Engineering, Westlake University, Hangzhou, China
{qiuhuachuan, lanzhenzhong}@westlake.edu.cn

Abstract—Dialogue systems are increasingly integrated into
mental health support to help clients facilitate exploration,
gain insight, take action, and ultimately heal themselves. For
a dialogue system to be practical and user-friendly, it should
be client-centric, focusing on the client’s behaviors. However,
existing dialogue systems publicly available for mental health
support often concentrate solely on the counselor’s strategies
rather than the behaviors expressed by clients. This can lead to
the implementation of unreasonable or inappropriate counseling
strategies and corresponding responses from the dialogue system.
To address this issue, we propose PsyChat, a client-centric dialogue system that provides psychological support through online
chat. The client-centric dialogue system comprises five modules:
client behavior recognition, counselor strategy selection, input
packer, response generator intentionally fine-tuned to produce
responses, and response selection. Both automatic and human
evaluations demonstrate the effectiveness and practicality of our
proposed dialogue system for real-life mental health support. Furthermore, we employ our proposed dialogue system to simulate
a real-world client-virtual-counselor interaction scenario. The
system is capable of predicting the client’s behaviors, selecting
appropriate counselor strategies, and generating accurate and
suitable responses, as demonstrated in the scenario.
Index Terms—dialogue system, client-centric, mental health
support, client behavior recognition, counselor strategy selection

I. I NTRODUCTION
Mental health [1] is a growing concern in our fast-paced and
digitally connected world. However, traditional mental health
support services often face challenges related to accessibility,
affordability, and stigma. Many individuals are hesitant to seek
help due to these barriers, leaving their mental well-being at
risk. With the increasing demand for mental health support,
there is a pressing need for innovative approaches to effectively
meet this demand.
Dialogue systems are increasingly integrated into mental
health support to assist clients in exploring, gaining insight,
taking action, and ultimately facilitating self-healing [2]. A
practical and user-friendly dialogue system should be clientcentric, focusing on the client’s behaviors. However, existing
dialogue systems [3]–[5] for mental health support often concentrate solely on counselors’ strategies, frequently overlooking the behaviors expressed by clients. This tendency leads
to the adoption of unreasonable or inappropriate counseling
strategies and corresponding responses from the dialogue
† Corresponding Author.

Counselor

( Supporting – Restatement )
You mentioned struggling with procrastination.
( Supporting – Affirmation and Reassurance )
It's impressive that you're aware of this tendency.

( Positive – Confirming )
I really appreciate your
understanding, and I do
realize that my
procrastination has become
a serious issue in my life.

( Negative – Sarcastic Answer )
You always provide me with
encouragement and support, and it feels
a bit like you're just going through the
motions. Any practical advice you can
offer?

Client

Client
( Challenging – Invite to
Explore New Actions )
Recognizing the issue is
the first step. What
strategies are you
considering to overcome
procrastination?

( Supporting – Reflection of Feeling )
Your desire to address this issue is
evident.
( Supporting – Inquiring Subjective
Information )
What specific guidance are you looking
for to combat procrastination?

Counselor

Counselor

Fig. 1. An illustration depicting how a counselor tailors strategies in response
to the behaviors exhibited by the client.

system. To be more specific, a practical and user-friendly
dialogue system should prioritize the consideration of clients’
states. Therefore, it should adjust its strategies based on the
clients’ current behaviors, mimicking human counselors, as
illustrated in Figure 1.
In light of this, we introduce P SY C HAT, a client-centric
dialogue system designed to provide psychological support
through online chat. The client-centric dialogue system consists of five modules: client behavior recognition, counselor
strategy selection, input packer, response generator, and response selection. The response generation module is intentionally fine-tuned using synthetic and real-life dialogue datasets.
The primary contributions of this paper are as follows:
To the best of our knowledge, we are the first to propose a
client-centric dialogue system for mental health support,
with a priority on considering the client’s behaviors.
• We optimize collaboration among modules by conducting
extensive experiments to identify the optimal model for
each. These selected models are then integrated to form
a cohesive dialogue system dedicated to mental health.
• Automatic and human evaluations demonstrate the effectiveness and practicality of our developed dialogue
system. Therefore, we release our code and model1 to
facilitate research in mental health support.
•

1 https://github.com/qiuhuachuan/PsyChat

1

Client Behavior Recognition

Dialogue
History

Client Behaviors

2

3

Input Packer

Counselor Strategies
Demonstrations

Counselor Strategy Selection

Input

4
Response
Generator

Optimal

Candidates

5
Response
Response
Selection

Dialogue History

(a) Front-end Web UI

(b) Back-end Dialogue System

Fig. 2. Architecture of the client-centric dialogue system. It contains two main parts: (a) Front-end web UI and (b) Back-end dialogue system, which is
implemented with Huggingface and FastAPI packages.

II. R ELATED W ORK
While the development of integrated dialogue systems for
mental health support remains unexplored, we provide a
summary of related work on constructing a dialogue system
for mental health support, examining it from the perspectives
of dataset and dialogue model.
a) Dataset: Due to privacy concerns in mental health,
the majority of dialogue datasets for mental health support
are obtained from public social platforms, crowdsourcing, and
data synthesis. Dialogue datasets collected from public social
platforms include the PsyQA dataset [3]. The crowdsourcing
process involves high costs and time, with the ESConv dataset
[4] being a typical example. Data synthesis is an effective
approach in the era of large language models, often resulting
in a large-scale corpus. Some typical datasets include AugESC
[5], SmileChat [6], and SoulChat [7]. Fortunately, a real-world
counseling dataset named Xinling [9] is conditionally opensourced and necessitates users interested in utilizing it to sign
a data usage agreement.
b) Dialogue Model: Open-source dialogue models for
mental health support contribute novel additions to the research community, including MeChat [6], SoulChat [7], and
ChatCounselor [8].
III. A C LIENT-C ENTRIC D IALOGUE S YSTEM FOR M ENTAL
H EALTH S UPPORT
The detailed architecture of the client-centric dialogue
system for mental health support is illustrated in Figure 2,
which comprises five modules: client behavior recognition
(§III-A), counselor strategy selection (§III-B), input packer
(§III-C), response generator (§III-D) intentionally fine-tuned
for response generation, and response selection (§III-E).
R
T
T
R
Given a dialogue history, {uT1 , uR
2 , u3 , u4 , ..., ut−1 , ut },
ending with the last utterance spoken by the client in a dialogue between a client and a dialogue system, the motivation
behind a client-centric dialogue system is to accurately identify
the client’s behavior and select the appropriate counseling
strategy. Here, T and R refer to the client and counselor,
respectively, derived from the last characters in the words
“client” and “counselor”. For brevity, this convention will be
consistently used thereafter and not repeated.

Therefore, to comprehend both clients’ behaviors and counselors’ strategies in text-based counseling conversations, we
propose utilizing the publicly available counseling conversational dataset, Xinling [9], which is annotated with rich
labels containing information on clients’ behaviors and counselors’ strategies. For detailed definitions of these labels,
please refer to the paper [9].
To enhance the practicality of the response generator for
mental health support, we advocate a two-stage fine-tuning
approach, considering the limited availability of actual counseling dialogues. In the initial phase, we propose using
a large-scale, close-to-real-life multi-turn dialogue dataset,
SmileChat [6], which is publicly accessible, for warmup parameter-efficient fine-tuning. Subsequently, to better
align with real-world application scenarios, we employ the
Xinling dataset, consisting of authentic dialogues, for the
second-stage downstream parameter-efficient fine-tuning.
Note that the classifier for client behavior recognition can be
used to label the client’s behaviors in SmileChat. However,
an auxiliary task is required to label the counselor’s strategies
in SmileChat. Therefore, we introduce an auxiliary task:
counselor strategy recognition, which is illustrated in §III-F.
A. Client Behavior Recognition
Considering a dialogue history ending with the last utterance
spoken by the client between a client and a dialogue system,
as illustrated in Equation 1, the goal is to accurately identify
the client’s behaviors.

T
R
R
T
Dh = uT1 , uR
2 , u3 , u4 , ..., ut−1 , ut

(1)

Therefore, the dialogue context can be formulated as follows:
T
R
R
Dc = {uT1 , uR
(2)
2 , u3 , u4 , ..., ut−1 }
To construct training, validation, and test sets, each sample
is represented as (xi , Yi ) ∈ Dclient , where Yi is the subset in
the label space of clients’ behaviors introduced in Xinling.
In reality, there are possibly multiple sentences in the client’s
utterance, and each sentence accordingly maps to a single
label. To simplify the classification task, we restructure the
clients’ lengthy utterances, originally labeled with multiple
categories, into pairs of sentences and corresponding labels.

Here is a demonstration with a detailed explanation.
Behaviors of the client's utterance, “You always provide me with
encouragement and support, and it feels a bit like you're just
going through the motions. Any practical advice you can offer?”,
include Sarcastic Question.
The counselor uses strategies, including Reflection of Feeling
and Inquiring About Subjective Information, to generate the
final response.
Demonstration:
Counselor: You mentioned struggling with procrastination. It's
impressive that you're aware of this tendency.
Client: You always provide me with encouragement and support,
and it feels a bit like you're just going through the motions. Any
practical advice you can offer?
Counselor: Your desire to address this issue is evident. What
specific guidance are you looking for to combat procrastination?

Model Prediction yi

FFNN + Softmax

1

2

512

Pre-trained Language Model
1

Tokenized Input [CLS]
Input

[CLS]

2

512

…

[SEP]

…

[SEP]

Dc

[SEP]

siT

[SEP]

Fig. 3. Mechanism of client behavior recognition.

Specifically, given a i-th sentence in the client’s utterance,
we can obtain (sTi , yi ), where sTi ∈ uTt and yi is the label
of clients’ behaviors. Thus, we represent the input for client
behavior prediction as follows:
xi = [Dc ; [SEP]; sTi ]

(3)

where [; ] denotes the operation of textual concatenation.
The mechanism of client behavior recognition is presented
in Figure 3. To facilitate a dialogue system in understanding
the client’s states, we train a fully-connected feed-forward
neural network (FFNN) with a softmax activation function
to identify clients’ behaviors based on a pre-trained language
model.

Now you act as a professional counselor and generate a
response for the following dialogue.
Behaviors of client's utterance, “You're being a little perfunctory.
But can you offer any specific guidance or recommendations?”,
include Sarcastic Answer.
The counselor uses strategies, including Reflection of Feeling
and Inquiring Subjective Information, to generate the final
response.
Dialogue:
Counselor: You mentioned that you often avoid your
responsibilities. It's good that you have this self-awareness.
Client: You're being a little perfunctory. But can you offer any
specific guidance or recommendations?
Counselor: I sense your determination to improve your situation.
What kind of advice or suggestions are you hoping to receive from
me?

Client’s
Utterance 𝑢!"
Client’s
Behaviors 𝑈&"
Counselor’s
Strategies 𝑈'(
Demonstration
𝐷%

Client’s
utterance 𝑢% !"
Client’s
#&"
Behaviors 𝑈
Counselor’s
#'(
Strategies 𝑈
Dialogue
#$
History 𝐷
Ground truth 𝑅#

Fig. 4. Template of the input packer. During the fine-tuning process, the
ground truth Rg is treated as a supervised signal. In the inference process,
our goal is to generate the response.

In this paper, we utilize the Euclidean distance as the metric,
which is a widely adopted similarity measure.

B. Counselor Strategy Selection
A dialogue session with a golden response spoken by the
counselor can be formulated as follows:

v
u n
uX
ˆ
d(Dh , Dh ) = t (qi − pi )2

(5)

i=1
T
R
R
T
Ds = {uT1 , uR
2 , u3 , u4 , ..., ut−1 , ut , Rg }

(4)

where Rg represents the ground truth (a.k.a golden response)
spoken by the counselor.
To recap, strategies behind a counselor’s response contain
explicit strategies with specific label descriptions and implicit
strategies hidden in the dialogue session. If we use a dialogue history to predict the counselor’s strategies, it may
seem contradictory to the task of recognizing client behavior.
Motivated by the notion that similar problems often have
analogous solutions, we employ dense retrieval to address
these challenges. This approach facilitates the dialogue system
in determining the most appropriate strategy by identifying
j semantically similar samples during both the training and
inference processes. For simplicity, we set j to 1.
Specifically, to obtain the most similar dialogue session,
we need to build a dialogue retrieval base Dbase . For each
dialogue session, Ds ∈ Dbase , we first split it into two parts:
the dialogue history Dh and the ground truth Rg . We then
construct mapping pairs {Sh , Rg } between dialogue history
Dh and ground truth Rg , where the string of a dialogue
T
R
R
T
history is denoted as Sh = [uT1 ; uR
2 ; u3 , u4 ; ...; ut−1 ; ut ].
ˆ
Therefore, when considering a brand-new dialogue history Dh ,
we propose to utilize the embedding model and apply dense
retrieval to find a sample with the most minimal distance.

where q and p represent mapping points of Dˆh and Dh in
Euclidean n-space, respectively.
a) Training Process: During the training process, since
we have access to the ground truth, we will directly incorporate
the counselor’s strategies.
b) Inference Process: However, during the inference
process, we cannot obtain the ground truth. Therefore, we will
adopt the counselor’s strategies from the retrieved sample for
response generation.
C. Input Packer
To better harness the inherent inference ability of large
language models, we redefine the conventional dialogue generation task using the instruction-following paradigm and address it through parameter-efficient fine-tuning. The template
of the input packer is illustrated in Figure 4, comprising two
main sections: a demonstration part and a brand-new dialogue
part, separated by a dashed blue line. Each section consists of
instructions and placeholders.
1) Instructions: The instructions serve to provide the model
with a well-defined role and precise task details for dialogue
generation. For the dialogue generation task, the text that is
non-bold and black is our instructions, as shown in Figure 4.

2) Placeholders: Based on the provided dialogue history
D̂h , which includes the client’s utterance ûTt and its context
D̂c in the brand-new dialogue segment, our first step is to
predict the client’s current behaviors ÛBT using the method
outlined in §III-A.
Furthermore, we will obtain the closest dialogue sample
using the method described in §III-B. Hence, we select the
closest sample as the demonstration Ds . Accordingly, we
obtain the client’s utterance uTt , the client’s behaviors UBT ,
and the counselor’s strategies USR for the demonstration part.
During the interference process, specifically in the brand-new
dialogue part, the counselor’s strategies ÛSR remain the same
as USR .
Packer Function: To summarize, the client input undergoes
processing through the packer function, as illustrated in Figure
4, before reaching the response generator. The process is
defined as follows:
It = P(uTt , UBT , USR , Ds , ûTt , ÛBT , ÛSR , D̂h )

(6)

where P(.) is the packer function used to format all input
elements into a string.

Task

# Train

# Validation

# Test

Client Behavior Prediction
Counselor Strategy Prediction

18824
21082

3683
4272

3627
4045

4) Response Generation: Moving forward, we input the
client’s input It into the response generator, which has been
trained through a two-stage fine-tuning process, to generate 10
response candidates.
E. Response Selection
Due to the uncertainty and diversity inherent in model generation, we propose adopting the sample-and-rank paradigm
to select the optimal response. To achieve this objective,
we suggest employing the widely used response selection
architecture: the Cross-encoder. This architecture facilitates
rich interactions between the dialogue history Dh and response
candidate Ri . We consider the first output of the pre-trained
language model as the history-candidate embedding:
yDh ,Ri = h1 = f irst(T r(Dh , Ri ))

D. Response Generator
1) Model: ChatGLM2-6B2 [10] is an open-source bilingual
(Chinese-English) chat model, which is trained with a context
length of 8192 during dialogue alignment, enabling more turns
in conversation. Therefore, we choose it as the foundational
model for our response generator.
2) Dataset Labeling: To adapt to the real-world downstream task, we fine-tune classifiers using the dialogue dataset,
Xingling, to annotate both the client’s and counselor’s
utterances, obtaining pseudo-labels for the dialogue dataset,
SmileChat. The mechanism of fine-tuning the classifier to
identify the behaviors of clients’ utterances and strategies
of counselors’ utterances is illustrated in §III-A and §III-F,
respectively.
3) Dialogue Generation: Considering our collected and
processed dataset Ddial = {D1 , D2 , ..., Di , ..., Dn }, where
each Di represents a single multi-turn dialogue, we split each
dialogue into multiple training sessions to train a response
generator. Specifically, for a sampled t-turn dialogue with
T
R
T
the ground truth, d = {uT1 , uR
2 , u3 , u4 , ..., ut , Rg } ∼ Ddial ,
we build a dialogue model that can predict the counselor’s
utterance Rg given the dialogue history Dh . We adopt the
input packer in §III-C to construct the training data {It , Rg }.
For both stages, the demonstration is retrieved from the
validation set in the dataset, Xinling. Our objective is to
maximize the likelihood probability as follows:

Ed∼Ddial

TABLE I
DATA STATISTICS OF BOTH CLIENT BEHAVIOR AND COUNSELOR
STRATEGY PREDICTION .

L
Y

P(Rg |It )

l=1

where L is the sequence length of the ground truth Rg .
2 https://huggingface.co/THUDM/chatglm2-6b

(7)

(8)

where f irst is the function that takes the first vector of the
sequence of vectors produced by the pre-trained language
model T r.
To score a candidate, a linear layer, denoted as W , is
applied to the embedding yDh ,Ri to transform it from a vector
to a scalar: s(Dh , Ri ) = yDh ,Ri W . The neural network is
trained to minimize cross-entropy loss, where the logits are
s(Dh , R1 ), ..., s(Dh , R10 ). Here, R1 represents the ground
truth, and the rest are negative samples randomly selected from
the training set.
F. Auxiliary Task: Counselor Strategy Recognition
A dialogue session with a response from the counselor can
be formulated as Ds = {Dh , Rg }. Referring to the §III-A,
we represent the input for counselor strategy prediction as
R
xi = [Dh ; [SEP]; sR
i ], where si is the i-th sentence in the
ground truth Rg .
IV. E XPERIMENTS
All experiments are performed using NVIDIA A100 8×80G
GPUs. For the tasks of client behavior recognition, counselor
strategy recognition, and response selection, we prepend a
speaker token, [client] or [counselor], to each utterance to identify the speaker.
A. Client Behavior Recognition
1) Data Statistics: After processing the data, we present
the statistics for client behavior prediction in Table I.
2) Hyperparameters: We employ a pre-trained Chinese
RoBERTa-large [11] model3 , commonly used for text classification. The hyperparameters used for fine-tuning the model
in client behavior recognition are provided in Table II.
3 https://huggingface.co/hfl/chinese-roberta-wwm-ext-large

TABLE II
H YPERPARAMETERS FOR FINE - TUNING THE MODEL USED FOR BOTH
CLIENT BEHAVIOR AND COUNSELOR STRATEGY RECOGNITION .
Hyperparameters

Values

Hyperparameters

Values

epochs

10

seed number

[42, 43, 44, 45]

batch size

16

warmup ratio

0.1

learning rate (lr)

2e-5

momentum values [β1 , β2 ]

[0.9, 0.999]

weight decay (λ)

0.01

dropout rate

0.1

TABLE III
R ESULTS OF CLIENT BEHAVIOR AND COUNSELOR STRATEGY PREDICTION
IN THE TEST SET, AS WELL AS RESPONSE SELECTION .
Task

42

43

44

45

Client Behavior Prediction
Counselor Strategy Prediction
Response Selection (R@1/10)

85.97
80.15
81.84

85.47
80.77
82.10

84.97
80.25
81.77

85.31
80.67
81.54

TABLE V
PARAMETERS OF PARAMETER - EFFICIENT FINE - TUNING .
Epoch

Learning
Rate

Batch
Size

LoRA
Rank

LoRA
Dropout

LoRA
α

Seed

2

1e-4

1

16

0.1

64

1234

TABLE VI
R ESULTS OF AUTOMATIC EVALUATION IN THE TEST SET.
PPL (⇓)

METEOR (⇑)

BLEU-1 (⇑)

Baseline

3.39

16.8

9.1

BLEU-2 (⇑)
4.1

Fine-tuning

1.26

22.9

24.1

11.6

BLEU-3 (⇑)

Rouge-L (⇑)

D-1 (⇑)

D-2 (⇑)

Baseline

1.5

12.4

62.1

87.8

Fine-tuning

5.3

27.6

86.6

97.2

E. Response Selection
TABLE IV
DATA STATISTICS . T HE DATASETS REPRESENTED BY ♢ AND ♣ ARE
UTILIZED FOR RESPONSE GENERATOR AND RESPONSE SELECTION ,
RESPECTIVELY.
Data Type

# Train

# Validation

# Test

SmileChat♢
Xingling♢
Xingling♣

310087
15850
15850

3150 (only used for retrieval)
3150

3072
3072

3) Results: We retain the best checkpoint with the highest
accuracy in the validation set for each seed. The results of
accuracy among the four seeds in the test set are reported in
Table III, demonstrating comparable accuracy to the original
paper. Therefore, we select the checkpoint trained with the
seed of 42 for client behavior recognition.
B. Counselor Strategy Selection
We utilize the publicly available embedding model
BAAI/bge-large-zh-v1.54 , accessible on Hugging
Face. The demonstrations used to construct the training and
test sets for response generation are retrieved from the validation set in Xinling, as illustrated in the second row of Table
IV. Furthermore, during the inference process, the counselor’s
strategy is also retrieved from the validation set in Xinling.
C. Input Packer
We provide data statistics for fine-tuning the response
generator in Table IV.
D. Response Generator
1) Parameter-efficient Fine-tuning: We apply the LowRank Adaption (LoRA [12]) to all linear layers in the
ChatGLM2-6B model for efficient fine-tuning. We present
the hyperparameters for fine-tuning the response generator in
Table V.
2) Dialogue Generation: During the generation process, we
set the maximum sequence length to 8192, the temperature to
0.8, and top_p to 0.8. Finally, we obtain 10 responses and
select the optimal one using our trained response selector.
4 https://huggingface.co/BAAI/bge-large-zh-v1.5

For the evaluation metric, we measure Recall@k, where
each test example has N possible response candidates to select
from, abbreviated as R@k/N . Since our objective is to select
the optimal response from these candidates, we set k to 1 and
N to 10.
1) Data Statistics: We present the data statistics for response selection in Table IV.
2) Hyperparameters: We utilize the hyperparameters for
response selection in Table II. One notable difference is that
we set the batch size to 1, considering that each batch consists
of one ground truth and 9 negatives.
3) Results: The results of response selection among the
four seeds in the test set are presented in Table III. Consequently, we choose the checkpoint trained with seed 43 for
response selection.
F. Auxiliary Task: Counselor Strategy Recognition
1) Data Statistics: We provide the data statistics for counselor strategy prediction in Table I.
2) Hyperparameters: We present the hyperparameters used
for fine-tuning the model in counselor strategy recognition in
Table II.
3) Results: We present the accuracy results for the four
seeds in the test set, as depicted in Table III. Consequently, we
select the checkpoint trained with seed 43 for data annotation.
V. R ESULTS
A. Automatic Evaluation
1) Metrics: To conduct automatic evaluation, we utilize
the following evaluation metrics: Perplexity (PPL) [13], METEOR [14], BLEU-1/2/3 [15], Rouge-L [16], and Distinct-1/2
(D-1/2) [17].
2) Results: We present the results of the automatic evaluation in Table VI. All the utilized automatic evaluation metrics
indicate enhanced performance, highlighting the effectiveness
and practicality of the proposed client-centric dialogue system.

TABLE VII
R ESULTS OF HUMAN EVALUATION OF 100 SAMPLES . T HE NUMBER OF
TIMES THAT EACH ANNOTATOR CHOOSES A MODEL RESPONSE AMONG
THREE MODELS : BASELINE , FINE - TUNED , AND GROUND TRUTH .
Annotator

Baseline

Fine-tuning

Ground Truth

Annotator A
Annotator B
Annotator C
Average

3
10
12
8

45
44
36
42

52
46
52
50

( Giving Information )
I've been feeling really down lately.
( Minimal Encouragement )
Hmm, you can tell me more. I'm here.
( Giving Information )
I'm finding it harder and harder to study, and I don't know what to do.
( Interpretation )
Yeah, so what I'm trying to say is, you might be feeling kinda lost right now, you know?
( Giving Information )
I can't stand myself right now, and I have no idea how to keep going with my studies.
( Affirmation and Reassurance )
Yeah, I think you're awesome. It's cool that you can recognize your own issues and
reach out for help.
( Reasonable Requesting )
Don't just comfort me like that, do you have any practical suggestions?
( Invite to Explore New Actions )
Right now, all you gotta do is tweak your mindset a bit. Get yourself outta this jam.

Fig. 5. An illustration of a case study of our client-centric dialogue system.

B. Human Evaluation
For human evaluation, we recruit three professional counselors, each with more than two years of counseling experience. Therefore, we sample 100 examples, each comprising
a dialogue history with three responses: the ground truth,
the response generated by the baseline model (without finetuning), and the response generated by our trained dialogue
system. The last two responses are selected from 10 candidates
by our response selector, respectively. Three counselors are
tasked with selecting the optimal response from three shuffled
options. They consider which one is more suitable for the
dialogue history from a holistic perspective.
We present the results of the human evaluation in Table
VII. The average selection times are 50, 42, and 8 for
ground truth, fine-tuning, and baseline, respectively. Three
professional counselors generally agree that the quality of
responses from our trained dialogue system is comparable
to that of the ground truth. Each annotator chooses a model
response. The chosen model scores 1 point, and the other two
models score 0 points. Thus, we can obtain three arrays of
300-dimensional scores and calculate the significant difference
between each pair of models separately. The results show that
only the p-value between the fine-tuned model and the ground
truth is 0.04, while the others are 0.00 (∗∗ p = 0.01). Therefore,
we conclude that the quality of responses generated by our
proposed dialogue system is comparable to the ground truth,
demonstrating the practicality and effectiveness of our clientcentric dialogue system.
C. Case Study
We present an illustration of a case study of our clientcentric dialogue system in Figure 5, demonstrating that our
dialogue system can better consider the client’s behavior.
With the ability to select an appropriate counseling strategy

and the assistance of response selection, our system can
provide a better response in real-world client-virtual-counselor
interaction scenarios.
VI. C ONCLUSION
In conclusion, the client-centric dialogue system for mental
health support represents a promising avenue for enhancing client well-being, addressing issues of accessibility, affordability, and stigma. Through both automatic and human
evaluations, our system has demonstrated its effectiveness
and practicality in real-life mental health support scenarios.
Notably, in a simulated client-virtual-counselor interaction,
the system successfully predicted client behaviors, selected
appropriate counselor strategies, and generated accurate and
suitable responses with the assistance of response selection.
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