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An Iterative Associative Memory Model for Empathetic Response Generation

Zhou Yang, Zhaochun Ren, Yufeng Wang, Chao Chen, Haizhou Sun, Xiaofei Zhu, Xiangwen Liao

TL;DR

The paper tackles empathetic response generation by modeling how humans iteratively comprehend emotional and cognitive states across dialogue. It introduces the Iterative Associative Memory Model (IAMM), which uses a second-order interaction attention mechanism to extract and store associated words from explicit dialogue content and implicit reasoning knowledge in a memory module. IAMM encodes both explicit information (utterances and situation) and implicit information (reasoning knowledge from COMET), then iteratively updates associations to improve emotion prediction and response generation. On Empathetic-DIALOGUES, IAMM achieves higher emotion accuracy and response diversity than strong baselines, and its large-language-model variants confirm the benefits of focusing on associated words for empathetic understanding and expression.

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.

An Iterative Associative Memory Model for Empathetic Response Generation

TL;DR

The paper tackles empathetic response generation by modeling how humans iteratively comprehend emotional and cognitive states across dialogue. It introduces the Iterative Associative Memory Model (IAMM), which uses a second-order interaction attention mechanism to extract and store associated words from explicit dialogue content and implicit reasoning knowledge in a memory module. IAMM encodes both explicit information (utterances and situation) and implicit information (reasoning knowledge from COMET), then iteratively updates associations to improve emotion prediction and response generation. On Empathetic-DIALOGUES, IAMM achieves higher emotion accuracy and response diversity than strong baselines, and its large-language-model variants confirm the benefits of focusing on associated words for empathetic understanding and expression.

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.
Paper Structure (27 sections, 17 equations, 4 figures, 4 tables)

This paper contains 27 sections, 17 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Task Formulation
  5. Overview
  6. Encoding Dialogue Information
  7. Capturing Associated Information
  8. Iterative Association Process
  9. Information Association Module
  10. Predicting Emotion and Generating Response
  11. Prediction Emotion
  12. Generation Response
  13. Baselines
  14. Implementation Details
  15. Evaluation Metrics
  16. ...and 12 more sections

Figures (4)

  • Figure 1: An example of iterative association. Words with the same color are associated. The memory stores the associated words.
  • Figure 2: Overview of IAMM and IAMM$_{large}$, which are small-scale and large-scale models focusing on association information, respectively. IAMM mainly consists of the following steps: (1) Encoding dialogue information, including explicit information (dialogue utterances and situation) and implicit information (reasoning knowledge); (2) Iteratively capturing associative information, namely associated words between sentences; (3) Predicting emotion and generating responses. Moreover, IAMM$_{large}$ focuses on subtle associations by injecting associated words into instructions.
  • Figure 3: Results of emotion analysis for associated words.
  • Figure 4: Results of frequency analysis for associated words.