An Iterative Associative Memory Model for Empathetic Response Generation

• URL: http://arxiv.org/abs/2402.17959v2
• Date: Sun, 2 Jun 2024 10:46:13 GMT
• Title: An Iterative Associative Memory Model for Empathetic Response Generation
• Authors: Zhou Yang, Zhaochun Ren, Yufeng Wang, Chao Chen, Haizhou Sun, Xiaofei Zhu, Xiangwen Liao,
• Abstract summary: Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances. We propose an Iterative Associative Memory Model (IAMM) for empathetic response generation.
• Score: 22.68709119989059
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that comprehending emotional and cognitive states necessitates iteratively capturing and understanding associated words across dialogue utterances. However, existing approaches regard dialogue utterances as either a long sequence or independent utterances for comprehension, which are prone to overlook the associated words between them. To address this issue, we propose an Iterative Associative Memory Model (IAMM) for empathetic response generation. Specifically, we employ a novel second-order interaction attention mechanism to iteratively capture vital associated words between dialogue utterances and situations, dialogue history, and a memory module (for storing associated words), thereby accurately and nuancedly comprehending the utterances. We conduct experiments on the Empathetic-Dialogue dataset. Both automatic and human evaluations validate the efficacy of the model. Variant experiments on LLMs also demonstrate that attending to associated words improves empathetic comprehension and expression.

Related papers

• 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)
• Dynamic Causal Disentanglement Model for Dialogue Emotion Detection [77.96255121683011]
  We propose a Dynamic Causal Disentanglement Model based on hidden variable separation. This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions. Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables.
  arXiv  Detail & Related papers  (2023-09-13T12:58:09Z)
• deep learning of segment-level feature representation for speech emotion recognition in conversations [9.432208348863336]
  We propose a conversational speech emotion recognition method to deal with capturing attentive contextual dependency and speaker-sensitive interactions. First, we use a pretrained VGGish model to extract segment-based audio representation in individual utterances. Second, an attentive bi-directional recurrent unit (GRU) models contextual-sensitive information and explores intra- and inter-speaker dependencies jointly.
  arXiv  Detail & Related papers  (2023-02-05T16:15:46Z)
• CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior [8.791757758576951]
  We propose a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors' emotions.
  arXiv  Detail & Related papers  (2023-02-03T14:31:17Z)
• Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection [47.60224978460442]
  We propose a Serial and Emotion-Knowledge interaction (SEEK) method for empathetic dialogue generation. We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response. Besides, we design a novel framework to model the interaction between knowledge and emotion to generate more sensible response.
  arXiv  Detail & Related papers  (2022-10-21T03:51:18Z)
• 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)
• Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes [50.569762345799354]
  We argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance and (ii) reflecting those specific words in the response generation. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label.
  arXiv  Detail & Related papers  (2021-09-18T04:22:49Z)
• Constructing Emotion Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation [22.2430593119389]
  We propose a dual-generative model, Dual-Emp, to simultaneously construct the emotion consensus and utilize some external unpaired data. Our method outperforms competitive baselines in producing coherent and empathetic responses.
  arXiv  Detail & Related papers  (2021-09-16T07:57:01Z)
• 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)
• Knowledge Bridging for Empathetic Dialogue Generation [52.39868458154947]
  Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. We propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation.
  arXiv  Detail & Related papers  (2020-09-21T09:21:52Z)

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.