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A Knowledge-Grounded Task-Oriented Dialogue System with Hierarchical Structure for Enhancing Knowledge Selection.

Hayoung Lee1, Okran Jeong1

  • 1School of Computing, Gachon University, 1342 Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a three-step system for task-oriented dialogue systems to improve knowledge utilization. It enhances response accuracy by effectively selecting relevant knowledge snippets for user queries.

Keywords:
classificationconversational AIknowledge selectionknowledge-grounded task-oriented dialogue systemnamed entity recognitionnegative samplingsnippet ranking

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Information Retrieval

Background:

  • Task-oriented dialogue systems require access to relevant knowledge to provide accurate user responses.
  • Existing systems often struggle with effectively integrating external, unstructured knowledge bases.
  • Internal knowledge bases (databases, APIs) are insufficient for all user requests.

Purpose of the Study:

  • To develop a novel three-step framework for knowledge-grounded task-oriented dialogue systems.
  • To enhance the system's ability to select the most appropriate knowledge snippet from a large knowledge base.
  • To improve the overall performance and efficiency of knowledge utilization in dialogue systems.

Main Methods:

  • A three-step approach: knowledge-seeking turn detection, knowledge selection, and knowledge-grounded generation.
  • Knowledge selection is hierarchically structured into domain classification, entity extraction, and snippet ranking.
  • Pre-trained language models with advanced techniques are employed for each task.
  • Domain and entity information is leveraged to refine the knowledge snippet search.

Main Results:

  • The proposed hierarchical knowledge selection significantly improves the performance and efficiency of identifying relevant knowledge.
  • Experimental results validate the effectiveness of the three-step system in selecting appropriate knowledge snippets.
  • The system demonstrates enhanced capabilities in grounding dialogue responses with external knowledge.

Conclusions:

  • The developed knowledge-grounded dialogue system effectively addresses the challenge of utilizing external unstructured knowledge.
  • The hierarchical approach to knowledge selection is a key innovation for improving dialogue system performance.
  • This framework offers a promising direction for building more knowledgeable and helpful conversational AI.