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Neural response generation for task completion using conversational knowledge graph.

Zishan Ahmad1, Asif Ekbal1, Shubhashis Sengupta2

  • 1Department of Computer Science and Engineering, AI-NLP-ML Lab, Indian Institute of Technology Patna, Patna, Bihar, India.

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Summary
This summary is machine-generated.

This study introduces a conversational knowledge graph to improve task-oriented dialogue systems. The novel approach enhances response consistency and context relevance, achieving better performance in dialogue generation.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Task-oriented dialogue systems require contextually relevant and consistent responses.
  • Generating diverse and multi-domain responses remains a challenge.
  • Existing models struggle to effectively capture conversational context.

Purpose of the Study:

  • To develop a novel method for enhancing dialogue generation in task-oriented systems.
  • To improve response consistency, relevance, and diversity.
  • To reduce reliance on extensive dialogue history for context capture.

Main Methods:

  • Developed six models using Bi-directional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT) encoders.
  • Implemented a copy mechanism for accurate slot value generation.
  • Introduced a conversational knowledge graph heuristic to capture dialogue state and context.

Main Results:

  • The conversational knowledge graph effectively captures essential conversational aspects.
  • Models utilizing the knowledge graph generated more relevant and consistent responses.
  • Achieved an average performance gain of 0.75 BLEU score compared to hierarchical encoder-decoder models.

Conclusions:

  • The conversational knowledge graph is an effective method for capturing dialogue context and user requirements.
  • This approach simplifies context management by relying on the last utterance.
  • The proposed method offers significant improvements in dialogue generation quality.