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ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling

Omama Hamad, Ali Hamdi, Khaled Shaban

TL;DR

ASEM tackles empathetic open-domain dialogue by combining sentiment and emotion analysis through a mixture of expert encoders and specialized attention. The two-stage design first learns sentiment- and emotion-aware feature representations, then generates empathetic responses via multiple decoders weighted by emotion signals. Empirical results on ED and DD show improved emotion classification accuracy ($\Delta F_1$ up to 6.2%) and higher lexical diversity, with human judges preferring ASEM across coherence, empathy, and fluency. The approach advances empathetic chatbot capabilities by producing contextually rich, emotion-aware embeddings suitable for real-world conversational agents.

Abstract

Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks. However, current approaches suffer from several drawbacks, such as the inability to capture the deep semantics of language and sensitivity to minor input variations, resulting in significant changes in the generated text. In this paper, we present a novel solution to these challenges by employing a mixture of experts, multiple encoders, to offer distinct perspectives on the emotional state of the user's utterance while simultaneously enhancing performance. We propose an end-to-end model architecture called ASEM that performs emotion analysis on top of sentiment analysis for open-domain chatbots, enabling the generation of empathetic responses that are fluent and relevant. In contrast to traditional attention mechanisms, the proposed model employs a specialized attention strategy that uniquely zeroes in on sentiment and emotion nuances within the user's utterance. This ensures the generation of context-rich representations tailored to the underlying emotional tone and sentiment intricacies of the text. Our approach outperforms existing methods for generating empathetic embeddings, providing empathetic and diverse responses. The performance of our proposed model significantly exceeds that of existing models, enhancing emotion detection accuracy by 6.2% and lexical diversity by 1.4%.

ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling

TL;DR

ASEM tackles empathetic open-domain dialogue by combining sentiment and emotion analysis through a mixture of expert encoders and specialized attention. The two-stage design first learns sentiment- and emotion-aware feature representations, then generates empathetic responses via multiple decoders weighted by emotion signals. Empirical results on ED and DD show improved emotion classification accuracy ( up to 6.2%) and higher lexical diversity, with human judges preferring ASEM across coherence, empathy, and fluency. The approach advances empathetic chatbot capabilities by producing contextually rich, emotion-aware embeddings suitable for real-world conversational agents.

Abstract

Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks. However, current approaches suffer from several drawbacks, such as the inability to capture the deep semantics of language and sensitivity to minor input variations, resulting in significant changes in the generated text. In this paper, we present a novel solution to these challenges by employing a mixture of experts, multiple encoders, to offer distinct perspectives on the emotional state of the user's utterance while simultaneously enhancing performance. We propose an end-to-end model architecture called ASEM that performs emotion analysis on top of sentiment analysis for open-domain chatbots, enabling the generation of empathetic responses that are fluent and relevant. In contrast to traditional attention mechanisms, the proposed model employs a specialized attention strategy that uniquely zeroes in on sentiment and emotion nuances within the user's utterance. This ensures the generation of context-rich representations tailored to the underlying emotional tone and sentiment intricacies of the text. Our approach outperforms existing methods for generating empathetic embeddings, providing empathetic and diverse responses. The performance of our proposed model significantly exceeds that of existing models, enhancing emotion detection accuracy by 6.2% and lexical diversity by 1.4%.
Paper Structure (22 sections, 13 equations, 4 figures, 10 tables)

This paper contains 22 sections, 13 equations, 4 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Problem Formulation
  5. Proposed Model
  6. Stage 1: Learning Empathetic Features Representation
  7. Stage 2: Generating Empathetic Response
  8. Experimental Setup
  9. Datasets
  10. Implementation Details
  11. Baselines
  12. Evaluation Metrics
  13. Results and Analysis
  14. Automatic Evaluation
  15. Human Evaluation
  16. ...and 7 more sections

Figures (4)

  • Figure 1: Emotional vs Sentimental Response.
  • Figure 2: Attention Weights.
  • Figure 3: ASEM model for empathetic chatbot.
  • Figure 4: Confusion matrix for MoEL (top) and ASEM (down) models for emotion analysis using ED dataset (0: Anger, 1: Fear, 2: Sadness, 3: Remorse, 4: Surprise, 5: Disgust, 6: Joy, 7: Anticipation, 8: Love, 9: Trust ).