Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality

• URL: http://arxiv.org/abs/2110.04614v1
• Date: Sat, 9 Oct 2021 17:12:41 GMT
• Title: Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality
• Authors: Jiashuo Wang, Wenjie LI, Peiqin Lin and Feiteng Mu
• Abstract summary: Empathetic response generation aims to comprehend the user emotion and then respond to it appropriately. Most existing works merely focus on what the emotion is and ignore how the emotion is evoked. We consider the emotional causality, namely, what feelings the user expresses and why the user has such feelings.
• Score: 13.619616838801006
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Empathetic response generation aims to comprehend the user emotion and then respond to it appropriately. Most existing works merely focus on what the emotion is and ignore how the emotion is evoked, thus weakening the capacity of the model to understand the emotional experience of the user for generating empathetic responses. To tackle this problem, we consider the emotional causality, namely, what feelings the user expresses (i.e., emotion) and why the user has such feelings (i.e., cause). Then, we propose a novel graph-based model with multi-hop reasoning to model the emotional causality of the empathetic conversation. Finally, we demonstrate the effectiveness of our model on EMPATHETICDIALOGUES in comparison with several competitive models.

Related papers

• ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause Reasoners through Reasoning Chains [61.50113532215864]
  Causal Emotion Entailment (CEE) aims to identify the causal utterances in a conversation that stimulate the emotions expressed in a target utterance. Current works in CEE mainly focus on modeling semantic and emotional interactions in conversations. We introduce a step-by-step reasoning method, Emotion-Cause Reasoning Chain (ECR-Chain), to infer the stimulus from the target emotional expressions in conversations.
  arXiv  Detail & Related papers  (2024-05-17T15:45:08Z)
• CTSM: Combining Trait and State Emotions for Empathetic Response Model [2.865464162057812]
  Empathetic response generation endeavors to empower dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. We propose Combining Trait and State emotions for Empathetic Response Model (CTSM) To sufficiently perceive emotions in dialogue, we first construct and encode trait and state emotion embeddings. We further enhance emotional perception capability through an emotion guidance module that guides emotion representation.
  arXiv  Detail & Related papers  (2024-03-22T10:45:13Z)
• The Good, The Bad, and Why: Unveiling Emotions in Generative AI [73.94035652867618]
  We show that EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. EmotionDecode reveals that AI models can comprehend emotional stimuli akin to the mechanism of dopamine in the human brain.
  arXiv  Detail & Related papers  (2023-12-18T11:19:45Z)
• E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation [33.57399405783864]
  We propose a novel emotion correlation enhanced empathetic dialogue generation framework. Specifically, a multi-resolution emotion graph is devised to capture context-based emotion interactions. We then propose an emotion correlation enhanced decoder, with a novel correlation-aware aggregation and soft/hard strategy.
  arXiv  Detail & Related papers  (2023-11-25T12:47:39Z)
• Empathetic Response Generation via Emotion Cause Transition Graph [29.418144401849194]
  Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion) We propose an emotion cause transition graph to explicitly model the natural transition of emotion causes between two adjacent turns in empathetic dialogue. With this graph, the concept words of the emotion causes in the next turn can be predicted and used by a specifically designed concept-aware decoder to generate the empathic response.
  arXiv  Detail & Related papers  (2023-02-23T05:51:17Z)
• 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)
• Empathetic Response Generation with State Management [32.421924357260075]
  The goal of empathetic response generation is to enhance the ability of dialogue systems to perceive and express emotions in conversations. We propose a novel empathetic response generation model that can consider multiple state information including emotions and intents simultaneously. Experimental results show that dynamically managing different information can help the model generate more empathetic responses.
  arXiv  Detail & Related papers  (2022-05-07T16:17:28Z)
• Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction [55.47134146639492]
  This article proposes a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post. Experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
  arXiv  Detail & Related papers  (2021-06-06T06:26:15Z)
• Enhancing Cognitive Models of Emotions with Representation Learning [58.2386408470585]
  We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions.
  arXiv  Detail & Related papers  (2021-04-20T16:55:15Z)
• Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation [25.808037796936766]
  In a real-world conversation, we instinctively perceive emotions from multi-source information. We propose a heterogeneous graph-based model for emotional conversation generation. Experimental results show that our model can effectively perceive emotions from multi-source knowledge.
  arXiv  Detail & Related papers  (2020-12-09T06:09:31Z)
• MIME: MIMicking Emotions for Empathetic Response Generation [82.57304533143756]
  Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content.
  arXiv  Detail & Related papers  (2020-10-04T00:35:47Z)

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.