ESCoT: Towards Interpretable Emotional Support Dialogue Systems

• URL: http://arxiv.org/abs/2406.10960v1
• Date: Sun, 16 Jun 2024 14:37:17 GMT
• Title: ESCoT: Towards Interpretable Emotional Support Dialogue Systems
• Authors: Tenggan Zhang, Xinjie Zhang, Jinming Zhao, Li Zhou, Qin Jin,
• Abstract summary: Previous works mostly focus on generating better responses but ignore interpretability, which is extremely important for constructing reliable dialogue systems. We propose an emotional support response generation scheme, named $textbfE$motion-Focused and $textbfS$trategy-Driven. We also conduct extensive experiments and human evaluations to validate the effectiveness of the proposed ESCoT and generated dialogue responses.
• Score: 57.19341456308303
• License: http://creativecommons.org/licenses/by/4.0/
• 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 $\textbf{E}$motion-Focused and $\textbf{S}$trategy-Driven $\textbf{C}$hain-$\textbf{o}$f-$\textbf{T}$hought ($\textbf{ESCoT}$), mimicking the process of $\textit{identifying}$, $\textit{understanding}$, and $\textit{regulating}$ emotions. Specially, we construct a new dataset with ESCoT in two steps: (1) $\textit{Dialogue Generation}$ where we first generate diverse conversation situations, then enhance dialogue generation using richer emotional support strategies based on these situations; (2) $\textit{Chain Supplement}$ where we focus on supplementing selected dialogues with elements such as emotion, stimuli, 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 $\href{https://github.com/TeigenZhang/ESCoT}{https://github.com/TeigenZhang/ESCoT}$.

Related papers

• Learning Retrieval Augmentation for Personalized Dialogue Generation [29.467644429517325]
  This paper studies the potential of leveraging external knowledge for persona dialogue generation. Experiments conducted on the CONVAI2 dataset with ROCStory as a supplementary data source show that the proposed LAPDOG method substantially outperforms the baselines.
  arXiv  Detail & Related papers  (2024-06-27T02:38:13Z)
• Acknowledgment of Emotional States: Generating Validating Responses for Empathetic Dialogue [21.621844911228315]
  This study introduces the first framework designed to engender empathetic dialogue with validating responses. Our approach incorporates a tripartite module system: 1) validation timing detection, 2) users' emotional state identification, and 3) validating response generation.
  arXiv  Detail & Related papers  (2024-02-20T07:20:03Z)
• Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs [59.74002011562726]
  We propose a novel linguistic cue-based chain-of-thoughts (textitCue-CoT) to provide a more personalized and engaging response. We build a benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English. Empirical results demonstrate our proposed textitCue-CoT method outperforms standard prompting methods in terms of both textithelpfulness and textitacceptability on all datasets.
  arXiv  Detail & Related papers  (2023-05-19T16:27:43Z)
• Re$^3$Dial: Retrieve, Reorganize and Rescale Dialogue Corpus for Long-Turn Open-Domain Dialogue Pre-training [90.3412708846419]
  Most dialogues in existing pre-training corpora contain fewer than three turns of dialogue. We propose the Retrieve, Reorganize and Rescale framework (Re$3$Dial) to automatically construct billion-scale long-turn dialogues. By repeating the above process, Re$3$Dial can yield a coherent long-turn dialogue.
  arXiv  Detail & Related papers  (2023-05-04T07:28:23Z)
• Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems [8.419582942080927]
  Response generation is one of the critical components in task-oriented dialog systems. We propose an approach that performs textit dynamic prompting where the prompts are learnt from dialog contexts. We show that contextual dynamic prompts improve response generation in terms of textit combined score citemehri-etal 2019-structured by 3 absolute points.
  arXiv  Detail & Related papers  (2023-01-30T20:26:02Z)
• Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning [81.79431311952656]
  We propose a novel system MultiESC to provide Emotional Support. For strategy planning, we propose lookaheads to estimate the future user feedback after using particular strategies. For user state modeling, MultiESC focuses on capturing users' subtle emotional expressions and understanding their emotion causes.
  arXiv  Detail & Related papers  (2022-10-09T12:23:47Z)
• HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data [87.67278915655712]
  We present a new dialogue dataset, HybriDialogue, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables. The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions.
  arXiv  Detail & Related papers  (2022-04-28T00:52:16Z)
• MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation [64.37111498077866]
  We propose a novel model for emotional support conversation. It infers the user's fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-03-25T10:32:04Z)
• DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation [33.53084158275457]
  RecentoL is an open-domain dialogue system that leverages conversational lines as controllable and content-planning elements to guide the generation model. Two primary modules inoL's pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines.
  arXiv  Detail & Related papers  (2021-02-03T18:36:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.