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EmoKbGAN: Emotion controlled response generation using Generative Adversarial Network for knowledge grounded

Deeksha Varshney1, Asif Ekbal1, Mrigank Tiwari2

  • 1Department of Computer Science & Engineering, Indian Institute of Technology Patna, Patna, India.

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This study introduces EmoKbGAN, a novel Generative Adversarial Network (GAN) approach for dialogue systems. EmoKbGAN enhances conversational engagement by focusing on sentence-level quality, incorporating emotion and knowledge for more human-like interactions.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Neural open-domain dialogue systems struggle with long-term engagement on popular topics.
  • Maximum Likelihood Estimation (MLE) methods for dialogue systems suffer from exposure bias.
  • Current systems often lack emotional depth and factual relevance in multi-turn conversations.

Purpose of the Study:

  • To develop a dialogue system capable of generating more socially engaging, long-term conversations.
  • To address the limitations of MLE-based approaches by focusing on sentence-level evaluation.
  • To improve response generation by incorporating emotional and knowledge-based attributes.

Main Methods:

  • Proposed EmoKbGAN, a Generative Adversarial Network (GAN) with multiple discriminators.
  • Utilized joint minimization of losses from knowledge and emotion-specific discriminator models.
  • Focused on sentence-level judgment rather than word-level evaluation to mitigate exposure bias.

Main Results:

  • EmoKbGAN significantly improved performance on benchmark datasets (Topical Chat, Document Grounded Conversation).
  • The model demonstrated superior results in both automated and human evaluations compared to baseline models.
  • Generated responses showed enhanced fluency with better control over emotion and content quality.

Conclusions:

  • EmoKbGAN offers a promising approach for creating more engaging and human-like dialogue systems.
  • Sentence-level training objectives are effective in improving dialogue generation quality.
  • Integrating emotion and knowledge components is crucial for socially engaging conversational AI.