Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation

• URL: http://arxiv.org/abs/2308.00085v2
• Date: Tue, 5 Sep 2023 05:45:30 GMT
• Title: Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation
• Authors: Yahui Fu, Koji Inoue, Chenhui Chu, Tatsuya Kawahara
• Abstract summary: We propose a commonsense-based causality explanation approach for diverse empathetic response generation. We enhance ChatGPT's ability to reason for the system's perspective by integrating in-context learning with commonsense knowledge.
• Score: 32.91998137372244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user's experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user's perspective, ignoring the system's perspective. In this paper, we propose a commonsense-based causality explanation approach for diverse empathetic response generation that considers both the user's perspective (user's desires and reactions) and the system's perspective (system's intentions and reactions). We enhance ChatGPT's ability to reason for the system's perspective by integrating in-context learning with commonsense knowledge. Then, we integrate the commonsense-based causality explanation with both ChatGPT and a T5-based model. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations.

Related papers

• Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning [12.766893968788263]
  Empathetic response generation endows agents with the capability to comprehend dialogue contexts and react to expressed emotions. Previous works predominantly focus on leveraging the speaker's emotional labels, but ignore the importance of emotion cause reasoning. We propose a cause-aware empathetic generation approach by integrating emotions and causes through a well-designed Chain-of-Thought prompt.
  arXiv  Detail & Related papers  (2024-08-21T13:11:03Z)
• Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs [57.16442740983528]
  In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback. The role of user feedback in annotators' assessment of turns in a conversational perception has been little studied. We focus on how the evaluation of task-oriented dialogue systems ( TDSs) is affected by considering user feedback, explicit or implicit, as provided through the follow-up utterance of a turn being evaluated.
  arXiv  Detail & Related papers  (2024-04-19T16:45:50Z)
• Crystal: Introspective Reasoners Reinforced with Self-Feedback [118.53428015478957]
  We propose a novel method to develop an introspective commonsense reasoner, Crystal. To tackle commonsense problems, it first introspects for knowledge statements related to the given question, and subsequently makes an informed prediction that is grounded in the previously introspected knowledge. Experiments show that Crystal significantly outperforms both the standard supervised finetuning and chain-of-thought distilled methods, and enhances the transparency of the commonsense reasoning process.
  arXiv  Detail & Related papers  (2023-10-07T21:23:58Z)
• Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge [39.536604198392375]
  In empathetic conversations, individuals express their empathy towards others. Previous work has mainly focused on generating empathetic responses by utilizing the speaker's emotion. We propose a novel approach for empathetic response generation, which incorporates an adaptive module for commonsense knowledge selection.
  arXiv  Detail & Related papers  (2023-05-24T10:25:12Z)
• CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation [10.22893584383361]
  We develop a new model, i.e., the Conditional Variational Graph Auto-Encoder (CVGAE), for the causality reasoning. We name the whole framework as CARE, abbreviated for CAusality Reasoning for Empathetic conversation. Experimental results indicate that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-11-01T03:45:26Z)
• CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation [59.8935454665427]
  Empathetic dialogue models usually consider only the affective aspect or treat cognition and affection in isolation. We propose the CASE model for empathetic dialogue generation.
  arXiv  Detail & Related papers  (2022-08-18T14:28:38Z)
• CEM: Commonsense-aware Empathetic Response Generation [31.956147246779423]
  We propose a novel approach for empathetic response generation, which leverages commonsense to draw more information about the user's situation. We evaluate our approach on EmpatheticDialogues, which is a widely-used benchmark dataset for empathetic response generation.
  arXiv  Detail & Related papers  (2021-09-13T06:55:14Z)
• Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication [88.52901763928045]
  We propose an approach that relies on exemplars to cue the generative model on fine stylistic properties that signal empathy to the interlocutor. We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics.
  arXiv  Detail & Related papers  (2021-06-22T14:02:33Z)
• DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations [0.0]
  We propose novel Contextual Reasoning Networks (DialogueCRN) to fully understand the conversational context from a cognitive perspective. Inspired by the Cognitive Theory of Emotion, we design multi-turn reasoning modules to extract and integrate emotional clues. The reasoning module iteratively performs an intuitive retrieving process and a conscious reasoning process, which imitates human unique cognitive thinking.
  arXiv  Detail & Related papers  (2021-06-03T16:47:38Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

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.