ESCoT: Towards Interpretable Emotional Support Dialogue Systems (2024)

Abstract: Understanding the reason for emotional support response is crucial for establishing connections between users and emotional support dialogue systems. Previous works mostly focus on generating better responses but ignore interpretability, which is extremely important for constructing reliable dialogue systems. To empower the system with better interpretability, we propose an emotional support response generation scheme, named Emotion-Focused and Strategy-Driven Chain-of-Thought (ESCoT), mimicking the process of identifying, understanding, and regulating emotions. Specially, we construct a new dataset with ESCoT in two steps: (1) Dialogue Generation where we first generate diverse conversation situations, then enhance dialogue generation using richer emotional support strategies based on these situations; (2) Chain Supplement where we focus on supplementing selected dialogues with elements such as emotion, stimulus, appraisal, and strategy reason, forming the manually verified chains. Additionally, we further develop a model to generate dialogue responses with better interpretability. We also conduct extensive experiments and human evaluations to validate the effectiveness of the proposed ESCoT and generated dialogue responses. Our data and code are available at https://github.com/TeigenZhang/ESCoT.

heading: 1 Introduction
text: Emotional support is conceptualized as expressing care, concern, affection, and interests, especially for the individuals feeling stressed or upset (Burleson, 2003; Albrecht and Adelman, 1987; Cutrona and Russell, 1987). Incorporating emotional support can yield positive effects in many scenarios, such as therapeutic sessions (Kennelly, 2001), customer service counters (Barnes and Halloway, 2005), and palliative cares (Skilbeck and
∗Co-first authors with equal contribution. †Corresponding Author.
Payne, 2003). Realizing reliable emotional support dialogue systems capable of automating these interactions is expected to expand the scope and efficacy of such services. Moreover, a reliable emotional support dialogue system should not work like a black box, providing conversational responses but unable to explain how those responses were generated (Gohel et al., 2021). As shown in Figure 1, let’s imagine how a helpful supporter would work by considering the feelings of a seeker who asks for help: the supporter would first identify the situation and the emotion of the seeker, then understand and acknowledge the emotion, and finally choose appropriate strategies to respond in order to regulate the emotion (Vincent J. D’Andrea, 1996). Therefore, it is extremely desired to build a reliable and trustworthy emotional support dialogue system that can not only generate emotional support responses but also provide the reasoning or chain-of-thought (CoT) behind the generated responses.
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Some previous endeavors have attempted to improve the interpretability of emotional support dialogue systems, such as controlling the response by emotion (Fu et al., 2023; Gao et al., 2021) or strategy (Cheng et al., 2022; Welivita and Pu, 2023), or using commonsense to augment the emotional support response (Wang et al., 2023c; Cai et al., 2023a). However, to the best of our knowledge, there is currently no such emotional support dialogue system that can provide comprehensive reasoning explanations. Therefore, in this work, we aim to build an interpretable emotional support dialogue system.
Due to the high expertise requirements for supporter roles in emotional support conversations, building a human-annotated emotional support dialogue dataset is very costly. Recently, the powerful language generation and reasoning capabilities of large language models (LLMs) have demonstrated a viable pathway to generate high-quality data. Efforts such as AUGESC (Zheng et al., 2023) and SMILECHAT (Qiu et al., 2023) have attempted to expand emotional support dialogue datasets via LLMs. However, the reasoning or chain-of-thought behind the dialogue responses has been overlooked.
In this paper, we propose an emotional support response generation scheme, named Emotionfocused and Strategy-driven Chain-of-Thought (ESCoT), to generate dialogue data, inspired by the human emotional support generation process of identifying, understanding, and regulating emotions. Specifically, to emphasize the critical role of conversation strategies and dialogue situations, we first create diverse dialogue situations, and then enhance dialogue generation using richer emotional support strategies based on these situations. Furthermore, we complement selected dialogues with the chain-of-thought (CoT), which is represented as a quintuple (EM,ES, IA, SR,RE), reflecting the process as illustrated in Figure 1. After careful manual checking, we build the first dataset for Emotional Support Dialogue with CoT (ESDCoT), containing 1.7k+ dialogues. Moreover, we build our emotional support dialogue system with better interpretability via supervised fine-tuning a pre-trained language model on ESD-CoT, providing a strong baseline for future investigation.
Our main contributions in this work include: (1) We develop an effective emotion-focused strategydriven chain-of-thought automatic data generation scheme called ESCoT to increase the interpretability of emotional support response generation. (2) We build the first chain-of-thought emotional sup-
port dataset ESD-CoT, containing 1.7k+ dialogues through automatic generation and manual correction. (3) We conduct human evaluations to validate from different aspects the effectiveness of our data generation scheme and the quality of our constructed dialogue dataset. (4) We build an interpretable emotional support response generation model on ESD-CoT and conduct a comprehensive assessment of the performance, providing a strong baseline for future research.

heading: 2 Related Work
text: Datasets Associated with Emotional Support Lack of sufficient datasets is one of the challenges faced by emotional support dialogue systems. Due to strict personal privacy protection requirements and high expertise demands, constructing high quality and diverse empathetic and emotional support dialogue datasets is extremely challenging for humans. Sharma et al. (2020) construct a dataset for supporting the EPITOME model based on TalkLife and Mental Health Subreddits. Sun et al. (2021) scrape Q&A from the Yixinli platform and annotate responses based on psychological counseling theories to create the PsyQA Chinese dataset.
These datasets are non-dialogue datasets, but applications like psychological counseling need multi-turn dialogues. Rashkin et al. (2019) propose a new emotional dialogue generation benchmark and create a new dataset called EMPATHETICDIALOGUES, which contains 25k dialogues in emotional contexts. Liu et al. (2021) propose the ESC framework and construct a dataset named ESConv based on this theoretical framework.
In order to obtain such datasets at a lower cost, some works leverage the power of LLMs to augment data for emotional support dialogues. Zheng et al. (2023) introduce AUGESC, an augmented dataset for the ESC (Liu et al., 2021) task by leveraging fine-tuned large language models to complete dialogues. Qiu et al. (2023) develop the SMILE approach by using ChatGPT (OpenAI, 2022) to transform single-turn dialogues into multi-turn conversations.
However, these works merely treat data augmentation as dialogue continuation or rewriting tasks, without making specific adjustments to accommodate the features of emotional support dialogues. For instance, dialogue situations and conversation strategies have not been taken into consideration.
Interpretable Dialogue Systems Deep learning models for dialogue systems are often seen as black boxes due to the complexity and opacity of their internal mechanisms. Lack of interpretability can lead to safety concerns, as it’s challenging to predict or understand the models’ decisions in critical scenarios. To address these concerns, researchers have been exploring different methods to improve the interpretability of language models.
One research direction involves integrating knowledge-based reasoning to improve the moral and ethical judgment capabilities of dialogue systems. Mehrabi et al. (2022) and Kim et al. (2022) try to incorporate external knowledge sources and structured reasoning pathways to enhance the decision-making quality of these models, particularly in scenarios requiring moral or ethical considerations. Li et al. (2023) introduce an interpretable dialogue system that employs a twostage response generation process, enhancing response diversity and system transparency. Moreover, Izumi et al. (2024) create dialogue modules based on CBT (Beck, 1979) dialogue scenarios centered on Socratic questioning and consider the questions about ABC (Ellis, 1991). An emerging direction is to explain language models using LLMs. Bills et al. (2023) explore using GPT-4 (Achiam et al., 2023) to interpret and understand the behavior of neurons in language models such as GPT-2 XL (Radford et al., 2018). However, there is currently no emotional support dialogue system that can provide comprehensive reasoning explanations to improve the interpretability.
Chain-of-Thought Prompting Wei et al. (2022) initially introduce Chain-of-Thought (CoT) Prompting to mimic the reasoning process. Following this, various works that utilize CoT to prompt LLM for intricate reasoning tasks spring up across different domains, such as Auto-CoT (Zhang et al., 2022), SP-CoT (Wang et al., 2023b), and PsyCoT (Yang et al., 2023). To address challenges when applying CoT prompting in dialogues, Dialogue CoT (Chae et al., 2023) propose decomposing commonsense reasoning into steps and generating rationale as a sequence of inferred commonsense knowledge required for response generation to facilitate Dialogue CoT reasoning. The Cue-CoT (Wang et al., 2023a) prompts the system to infer the user status first and then generate a response based on dialogue context and user status. However, CoT for emotional
support dialogue systems has not been well explored yet.

heading: 3 ESD-CoT Dataset Construction
text: Due to the high expertise requirement in the supporter role, manually constructing an emotional support dialogue dataset is very costly. Mimicking the human thought process of comforting others, we propose an Emotion-focused and Strategydriven Chain-of-Thought (ESCoT) scheme to generate emotional support responses in an interpretable manner, and build the first Emotional Support Dialogue with CoT dataset (ESD-CoT).
The construction process of ESD-CoT dataset can be divided into two steps: (1) ESD Construction where we first create diverse situations, and then generate dialogues with enriched strategies based on generated situations by leveraging LLMs; (2) ESD-CoT Construction where we first generate reasoning chains of the selected dialogues via LLMs, and then conduct manual verification and modification to ensure the accuracy of the chains.

heading: 3.1 ESD Construction
text: Considering the critical role of situation and strategy in emotional support dialogues, we propose a situation- and strategy-guided dialogue generation scheme based on large language models to build a situation-diverse, strategy-rich Emotional Support Dialogue dataset (ESD). Specifically, we
first generate a more diverse range of psychological counseling situations and enrich the existing strategies used in (Liu et al., 2021). Then, we generate emotional support dialogues with the guidance of different situations and enriched emotional support strategies. Furthermore, we conduct extensive data analysis to demonstrate the quality of the generated data, and also conduct human evaluation to validate the necessity of strategy enrichment.

heading: 3.1.1 Situation Generation
text: The situations reflect the issues for which seekers are asking for help. In order to produce highquality emotional support dialogues, various realistic psychological counseling situations are crucial. Inspired by (Wang et al., 2023d), we leverage the in-context learning ability of ChatGPT for extensive and diverse situation generation.
We initialize the original situation pool with 1,300 manually annotated situations in ESConv (Liu et al., 2021), forming a seed pool. We use the in-context learning method and design a situation generation prompt for ChatGPT. For each generation iteration, we randomly select eight situations from the seed pool as in-context examples and generate eight new situations adding to the seed pool. More details of the situation generation prompt are presented in Appendix A.1.
To ensure high-quality and diversity of the generated situations, we remove duplicate situations and filter out inadequate situations that lack personal pronouns or have incomplete sentences etc. Finally, 2,943 new situations are retained to enrich the generation of subsequent dialogue data.

heading: 3.1.2 Strategy Enrichment
text: Eight strategies are employed in the ESConv dataset (Liu et al., 2021), while other important strategies which are useful for emotional support have not yet been employed. Considering the significance of strategies in practical counseling, we therefore are motivated to further enrich the existing eight strategies established by ESConv.
Based on suggestions from experienced psychological counselors, we enrich the strategies following three principles: (1) Distinct: make sure each strategy focuses on different aspects compared to existing strategies. (2) Understandable: make sure each strategy is concise and comprehensible, even if it does not come with a short description. (3) Identifiable: make sure the implementation of the strategy can be easily identified from a few sen-
tences. Following the above principles and under the guidance of experts, we extract six strategies from helping skills (Hill, 2009), including Summarize, Imagery, Specify, Take Responsibility, Homework Assignment, and Immediacy.
Note the focus of different strategies varies. For example, Summarize is a general summary of the whole conversation, while Restatement or Paraphrasing focuses on a simple restatement of the content just mentioned. Homework Assignment is a type of direct guidance, which directly tells the seeker what to do, while Providing Suggestions doesn’t directly tell the seeker what to do. Through the expanded diverse strategies, we can generate higher quality dialogues, demonstrated by the human evaluation in 3.1.4. Detailed definitions and examples of these enriched strategies are provided in the Appendix A.2.

heading: 3.1.3 Dialogue Generation
text: After obtaining sufficient situations and richer emotional support strategies, we proceed to generate emotional support dialogues based on these situations and strategies.
Prompt Template The prompt format used for generating new dialogues is shown in Figure 3. We first introduce the role of a therapist and describe the task. Then, an illustrative example is provided, including Reference Situation and Reference Dialogue, serving as a template for the format and content of the dialogues we expect ChatGPT to generate. Next, the Target Situation is introduced, derived from the previously generated situation pool,
to provide a specific scenario for ChatGPT to engage with. Subsequently, there is a brief restatement of the goal of the dialogue generation task to enhance ChatGPT’s comprehension of the task. In addition to the above task description and format reference, strategy is introduced as a crucial component, enhancing the professionalism and reliability of the generated dialogues.
Filtering and Postprocessing We find four types of undesirable dialogues from inspecting cases of the generated dialogues: (1) Inadequate Interaction Rounds; (2) Presence of Empty Utterances; (3) Insufficient Strategic Annotations; (4) Divergence from Prescribed Strategies. In order to guarantee both quantity and diversity, we regenerate filtered dialogues. We keep performing the filtering and regeneration process until finally each of the situations is paired with a high-quality dialogue. After filtering and postprocessing all the generated data, we retain dialogues with diverse and richer strategies to form our ESD dataset.

heading: 3.1.4 Statistics of ESD
text: The overall statistics of our generated emotional support dialogue (ESD) dataset are shown in Table 1. Compared to ESConv, our dataset is larger in scale. We also show some breakdown statistics in terms of conversation roles. Our supporter portion is significantly longer than ESConv. This aligns with our goal of enhancing the quality of responses from supporter roles in the dialogue system.
Diversity Analysis We analyze the diversity of our ESD from situation and dialogue perspectives. Situation Diversity: We assess the diversity of issues faced by seekers in the situations through word frequency analysis. The topic diversity of situations is shown in Figure 4. Our ESD dataset not only encompasses a wider range of everyday conversational themes, but also places a greater emphasis on topics related to mental well-being.
Dialogue Diversity: Following Zheng et al. (2023), we calculate the z-scored log odds ratio values relative to ESConv, to extract the topic features of dialogues. By analyzing the salient words of the datasets, we discover that different from ESConv which focuses solely on informal conversations and interpersonal dynamics, dialogues in our ESD are more specific, offering more professional information, and diving deeper into topics related to mental health and personal challenges. More details of the dialogue diversity can be found in Appendix A.3.
Strategy Analysis We first assess the impact of utilizing strategies in dialogue generation, and then present the statistics of application frequency of different strategies and distribution of strategies across different stages of dialogues. Strategy Impact Assessment: To assess the impact of incorporating strategies into prompts on the quality of generated dialogues, we design and compare three different prompts: one without adding strategies, one incorporating strategies solely from ESConv, and one adding enriched strategies. Specifically, we randomly select 60 situations from ESConv and use these prompts to generate dialogues for each of these situations separately. Subsequently, we recruit 15 individuals with psychological counseling backgrounds to rank the dialogues. Following the six dimensions in AUGESC (Zheng et al., 2023), we ask the evaluators to rank the dialogues based on the given dimensions. The results are shown in Table 2. The quality of dialogues generated by ChatGPT surpasses ESConv across
nearly all dimensions, regardless of whether strategies are applied. And incorporating our enriched strategies can generate the best dialogues in all dimensions. More details of prompts for strategy impact assessment can be found in Appendix A.4. Strategy Application Frequency: We count the usage frequency of each strategy and the proportions in Table 3. We can see that no enriched strategy shows very low frequency, indicating that our enriched strategies are well utilized in dialogue generation. Employing various strategies in emotional support dialogue generation enhances dialogue diversity and better simulates real-life interactions.

heading: 3.2 ESD-CoT Construction
text: Since a lack of model interpretability affects people’s trust in the model, enhancing model interpretability is a key aspect in building a reliable empathetic dialogue system for people seeking emotional support. We propose the Emotion-focused and Strategy-driven Chain-of-Thought (ESCoT) that mimics the human consultation process of identifying, understanding and regulating emotions. In this framework, understanding emotions encompasses both the emotional stimulus and the indi-
vidual’s appraisal based on the cognitive appraisal theory (Lazarus, 1991), while regulating emotions includes the strategy reason and the response generation. In general, the CoT is represented as a quintuple (EM,ES, IA, SR,RE). In this section, we supplement the chain-of-thought based on the previously generated dialogue data, and build the first dataset for Emotional Support Dialogue with CoT (ESD-CoT).

heading: 3.2.1 Chain Creation
text: We first automatically generate the CoT and then conduct manual correction in order to significantly reduce the annotation cost. The specific meaning of each element of the quintuple is as follows:
• Emotion (EM) denotes the emotion expressed by the seeker.
• Emotion Stimulus (ES) refers to the specific trigger that evokes the seeker’s current emotion, which can be external, such as a situation or event, or internal, such as a thought or memory.
• Individual Appraisal (IA) denotes the seeker’s personal interpretation, evaluation, and internal response to the emotion stimulus, based on the seeker’s past experiences, beliefs, expectations, and personal values.
• Strategy Reason (SR) represents the reason why the supporter used the chosen strategy in the last utterance.
• Response (RE) denotes the response provided by the supporter in the ongoing dialogue.
More details about the CoT generation template are presented in Appendix B.

heading: 3.2.2 Manual Correction
text: After generating preliminary CoT using ChatGPT, we conduct manual correction to ensure the quality. Based on the definition of each element, we
identify several issues in the raw CoT data: (1) Unclear Emotional Expression; (2) Lacking Specific Examples; (3) Verbose Personal Evaluations; (4) Mismatch between Strategies and Responses. Therefore, we perform manual corrections to these issues. Additionally, we filter out toxic data to enhance the security and ethics of the dataset. After processing all the generated data, we finally construct our ESD-CoT dataset with a total of 1,708 dialogues accompanied with CoT, explaining why and how emotional support responses are generated.

heading: 3.2.3 Statistics of ESD-CoT
text: Table 4 presents the statistics of our ESD-CoT dataset. The relatively long dialogue turns and length of ESD-CoT provide sufficient information for CoT analysis. The length of EM indicates that the seeker often expresses multiple emotion in the dialogue. The length of IA is more than twice that of ES, as IA provides a more detailed personal appraisal based on ES, involving more cognitive processing and emotional expression. The relatively long SR provides detailed explanations about the reasons for the chosen strategy, potentially enhancing interpretability. Table 5 shows the distribution of strategies in ESD-CoT dataset. We ensure that each strategy has at least 100 samples in our ESD-CoT dataset. Overall, our dataset ESDCoT is richer in content and strategy, and provides interpretable CoT annotations, thereby enhancing the credibility of emotional support responses.
Strategy Train Val Test Total
Reflection of Feelings 68 13 19 100 Question 89 15 20 124 Providing Suggestions 123 12 41 176 Summarize 73 11 16 100 Specify 86 21 30 137 Restatement or Paraphrasing 83 9 21 113 Homework Assignments 66 11 24 101 Affirmation and Reassurance 224 31 63 318 Imagery 88 8 26 122 Information 74 11 29 114 Self-disclosure 75 11 14 100 Immediacy 76 8 19 103 Take Responsibility 70 11 19 100
Total 1195 172 341 1708

heading: 4 Experiments
text: We split the dataset into train, validation, and test with the ratio of 7:1:2, as shown in Table 5. We evaluate the following pre-trained language models as backbone models for dialogue response
generation: (1) BlenderBot (Roller et al., 2021); (2) DialoGPT (Zhang et al., 2020); (3) LLAMA2CHAT (Touvron et al., 2023).
Evaluation Metrics We apply three commonly used automatic evaluation metrics, BLEU-n (Papineni et al., 2002), ROUGE-L (Lin, 2004); and Distinct-n (Li et al., 2016).
As for human evaluations, following Tu et al. (2022) and Cai et al. (2023b), we recruit 3 professional annotators to evaluate randomly selected 50 responses of different settings from the Coherence, Informativeness, and Empathy aspects with the levels of {0,1,2}. We also conduct a human evaluation to evaluate the Accuracy of the consistency between the selected strategy and the corresponding response. More details of human evaluations can be found in Appendix C.

heading: 4.1 Comparison of Backbone Models
text: To enable chain generation with better interpretability, we fine-tune backbone models on ESD-CoT train set and report the performance of chain generation on the test set in Table 7. LLAMA2-CHAT outperforms other backbone models on all metrics, which can be attributed to larger parameters, more training data, and the utilization of reinforcement learning with human feedback in LLAMA2-CHAT. Specially, since the process of ESCoT involves reasoning, we believe the reasoning ability that emerges from increasing parameters is very important for interpretable emotional support. Due to its excellent performance, we conduct the ablation
study based on LLAMA2-CHAT in the following subsection. More implementation details of supervised fine-tuning can be found in Appendix D.1.

heading: 4.2 Ablation Study
text: To explore the effects of different elements of the ESCoT on the generated response, we conduct the ablation study and report the results in Table 6. Specially, we remove some nodes of the chain and train models for each setting, and calculate the metrics only based on the RE part of the model outputs. More implementation details of the ablation study can be found in Appendix D.2.
Generation with the entire chain, as outlined in the first row, achieves the best performance on metrics D-1 and D-2, which may be due to the fact that many steps in the entire CoT chain cause relatively greater randomness in response generation, leading to better diversity in the generated responses. We also notice that the setting of directly generating responses in the fourth row achieves the best results on B-1, B-2, and R-L, which are primarily used to assess the similarity between the prediction and ground truth. As the automatic metrics are calculated only based on the RE part, directly fitting the RE part yields the best results.
However, BLEU and ROUGE primarily focus on the similarity between the prediction and ground truth, which can not effectively measure the effects of emotional support. So, we conduct human evaluations to assess the effects of different ablation settings better. The setting of the complete chain in the first row achieves the highest performance in all human evaluation dimensions, indicating that it not only provides the most comprehensive interpretability but also delivers more coherent, informative, and empathetic responses. As for the strategy consistency assessment, the highest accuracy is also achieved by the full chain. Removing the emotion understanding component in the second row decreases strategy consistency, and further
removing the emotion identification component in the third row leads to an additional decline in strategy consistency. We demonstrate a case study for the ablation study in Appendix G.
In summary, the whole chain exhibits the best response in major aspects and offers the most comprehensive interpretability, providing a strong baseline and feasible path towards interpretable emotional support dialogue systems.

heading: 5 Conclusion
text: In this paper, inspired by the human consultation process of identifying, understanding, and regulating emotions, we propose a novel emotion-focused and strategy-driven chain-of-thought (ESCoT) emotional support response generation scheme and apply it in building the first dataset for emotional support dialogue with chain-of-thought named ESD-CoT. Moreover, we conduct extensive experiments and human evaluations to validate that the new benchmark based on ESD-CoT can provide effective emotional support with better interpretability. We hope our ESD-CoT dataset and baseline models can facilitate further investigation into interpretable emotional support dialogue systems in the community.
Ethics Statements
The interpretability of emotional support dialogue systems has become increasingly important in real applications. We deeply recognize the need for caution in developing datasets related to ethical issues. Our goal is to create an interpretable emotional support dialogue dataset and system. During the construction of the dataset, we strictly adhere to data source usage agreements, making diligent efforts to eliminate any biased, offensive, or inappropriate content to avoid potential unpredictable ethical harm. All human annotators and evaluators are paid according to their individual working hours. We hope that this dataset will enhance the transparency and credibility of emotional support system responses, thereby establishing a bridge of trust and understanding between users and emotional support dialogue systems.
Limitations
Although we reduce a lot of costs by utilizing ChatGPT during the generation phase, the scale of our dataset remains relatively small due to limitations in the cost of manual correction. Future
work could focus on designing more automated correction methods to reduce the manual component and lower the cost of dataset construction even more. To enhance the diversity of the generated dialogue data, we incorporate rich situations and expanded strategies into the prompt. We plan to further enhance the diversity of the emotional support dialogue dataset by introducing more personalized information, such as personality. Furthermore, the strategy annotation of current public emotional support datasets only focuses on single strategies, neglecting the compound strategies, and we annotate the major strategy when facing multiple strategies. We consider exploring compound strategies to prove more effective emotional support in the near future.

heading: Acknowledgements
text: We thank all reviewers for their insightful comments and suggestions. This work was partially supported by the National Natural Science Foundation of China (No. 62072462) and the Beijing Natural Science Foundation (No. L233008).

heading: A Details of ESD Dataset Construction
text: A.1 Prompt for Situation Generation Inspired by Ouyang et al. (2022), we use the prompt in Figure 6 to generate new situations.
A.2 Definitions and Examples of Enriched Strategies
Through our enriching process, we double the size of strategies based on ESConv. Table 8 presents the entire list of strategies that are employed in our emotional support dialogue generation, including both definition and example for each strategy.
A.3 Details of Dialogue Diversity Analysis We calculate the log odds ratio values for the words of ESD relative to ESConv, which is formulated as follows:
LogOddsRatio(i) = log((pi,1/(1−pi,1))/(pi,2/(1−pi,2)) (1) where pi,1 and pi,2 represent the probabilities of the word i in two compared datasets. Based on the log odds ratio values, we calculate the z-score as follows:
z-score(i) = (LogOddsRatio(i)− µ)/σ (2)
where µ is the mean of all words’ log odds ratio, and σ is the standard deviation.
We rank all words based on their z-scores. The top 25 significant words and their z-scores for each
dataset are shown in Table 9. In the comparison between two datasets, the word with higher z-scores can represent a greater distinction within its dataset compared to the other dataset.
In the ESConv dataset, words such as ‘wow’, ‘mad’, ‘sucks’, and ‘horrible’ reflect emotional states and reactions. Additionally, word like ‘counselling’, ‘grade’, ‘zoom’ and ‘survey’ point to various types events in school. Words like ‘covid’, ‘vaccine’, ‘virus’, and ‘holiday’ suggest discussions about time and significant events. Expressions like ‘lol’, ‘bye’, and ‘ya’ indicate informal and colloquial conversations.
In contrast, our dataset contains words like ‘resilience’, ‘visualizing’, ‘caregiving’, and ‘vision’, which relate to coping strategies and empowerment. Words like ‘gender’, ‘sexuality’, and ‘accommodations’ indicate a theme related to identity and inclusivity. The presence of words like ‘substance’, ‘cognitive’, and ‘culture’ suggest that there are discussions focusing on psychological and cultural aspects in ESD dataset.
The analysis of these significant words shows that ESConv dataset are more general, focusing on everyday life, emotions, and informal interactions. In contrast, our dataset is more specific and related to more professional guidance, diving into subjects related to mental health and personal challenges deeper.
A.4 Prompts for Strategy Impact Assessment
To assess the impact of incorporating strategies into prompts on the quality of generated dialogues, we design three different prompts: (a) without adding strategies, (b) incorporating strategies solely from ESConv, and (c) adding our enriched strategies. Only the instruction parts of these prompts are different, which are outlined in Figure 7 with the differences highlighted.

heading: B Chain Creation Prompt of ESD-CoT Dataset
text: The prompt template used for generating chains is shown in Figure 8. We first introduce the task and give the definition of each element. Then, we provide an example, which will be replaced with the chain example in the example pool. Furthermore, we provide the dialogue, which needs to be supplemented with the chain. Finally, we standardize the format of the output. Note the target strategy will be replaced with the strategy used in the formerly
generated dialogue.

heading: C Details of Human Evaluation
text: As for the response evaluation, to fairly evaluate different responses, we randomly shuffle the responses when evaluated by annotators. We pay for these 3 annotators, and all the results are proofchecked by an expert.
As for the strategy consistency evaluation, we randomly choose 100 chains for the settings containing SR and recruit 2 professional annotators to evaluate the consistency and another professional annotator to deal with the situation where two annotators’s assessments are inconsistent.
D Implementation Details
D.1 Supervised Fine-Tuning BlenderBot is an open-domain conversational agent equipped with a range of communication
skills, including empathetic response capabilities. For our experiments, we use the small† version of BlenderBot. We train the BlenderBot model based on the code of ESConv† (Liu et al., 2021). We train the model using one A6000 GPU, with a batch size of 64, a learning rate of 3e-5, and a max sequence length of 500.
DialoGPT is a model built upon the foundation of the dialogue generative pre-trained transformer, specially, GPT-2 (Radford et al., 2018). For our experiments, we use the small† version of DialoGPT. We train the DialoGPT model based on the code of ESConv (Liu et al., 2021). We train the model using one A6000 GPU, with a batch size of 32, a learning rate of 5e-5, and a max sequence length of 500.
LLAMA2-CHAT is a pre-trained model optimized for dialogue use cases, which has been specifically designed to adapt to a wide range of conversational scenarios. For our experiments, we
†https://huggingface.co/facebook/BlenderBotbot_small90M
†https://github.com/thu-coai/Emotional-SupportConversation
†https://huggingface.co/microsoft/DialoGPT-small
use the 7B version of LLAMA2-CHAT on Hugging Face. The training of LLAMA2-CHAT model is based on the SFT trainer of Transformer Reinforcement Learning† (von Werra et al., 2020). We train the model using 4*A6000 GPUs, with batch size of 8 per GPU, learning rate of 5e-5, and max sequence length of 2048.
D.2 Ablation Study We conduct the ablation study based on LLAMA2CHAT. The model size and parameters are the same as those in Appendix D.1. The only difference between different settings is the composition of the data. For example, in the setting of {EM,SR,RE}, the instruction is "Generate the response as the supporter using the pipeline of Emotion, Strategy Reason, Response." and the corresponding ground truth consists of the manually checked Emotion, Strategy Reason, and Response. For each setting, we train the model for 10 epochs using 4*A6000 GPUs for approximately 3 hours, with batch size of 8 per GPU, learning rate of 5e-5, and max sequence length of 2048 on the training dataset, and select the best-performing checkpoint
†https://github.com/huggingface/trl
based on the validation dataset to obtain its performance metrics on the test dataset.

heading: E Examples of ESD Dataset
text: We present examples of the ESD dataset in Figure 9 and Figure 10, including situations and the generated dialogues. The strategies originally present in ESConv are labeled in green, while the strategies we enriched are marked in red.

heading: F Examples of ESD-CoT Dataset
text: We present examples of the ESD-CoT dataset in Figure 11 and Figure 12. The upper part is the context of the dialogue, and the lower part is the chain to generate the supporter’s response. The chain and the corresponding part of the dialogue are highlighted in the same color.

heading: G Case Study
text: In Table 10, we demonstrate a case of the ablation study. In this case, the seeker feels trapped and isolated in a toxic relationship and unsure about what the right thing to do is. The model’s responses from different incomplete chain settings are focused only on one aspect. In the full chain setting, the model’s response considers empathy and emphasizes the importance of personal responsibility. It encourages the seeker to make decisions that align with their values and goals and to clarify the next steps. Besides, the reason for the generated response can be clearly seen in the setting of the entire chain.

heading: 1. seeker: I'm really scared. I've been charged with a crime and I don't know what to do next.

heading: 1. seeker: I'm battling chronic procrastination and experiencing difficulty sticking to schedules and deadlines, which are hampering both my personal and professional productivity.

heading: Future Work
text: Future
work could focus on designing more automated correction methods to reduce the manual component and lower the cost of dataset construction even more. To enhance the diversity of the generated dialogue data, we incorporate rich situations and expanded strategies into the prompt. We plan to further enhance the diversity of the emotional support dialogue dataset by introducing more personalized information, such as personality. Furthermore, the strategy annotation of current public emotional support datasets only focuses on single strategies, neglecting the compound strategies, and we annotate the major strategy when facing multiple strategies. We consider exploring compound strategies to prove more effective emotional support in the near future