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Building a Discourse-Argument Hybrid System for Vietnamese Why-Question Answering.

Chinh Trong Nguyen1, Dang Tuan Nguyen2

  • 1University of Information Technology, VNU-HCM, Ho Chi Minh City, Vietnam.

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Deep learning models struggle with why-questions. This study introduces a novel approach using discourse analysis and natural language inference to improve explanations for why-questions, increasing the answer rate.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Deep learning models achieve high F1 scores on question answering tasks, but perform poorly on why-questions.
  • Existing models struggle to provide explanations, achieving F1 scores of 0.57-0.7 on SQuAD v1.1 for why-questions.
  • Why-questions require explanations, which can be arguments or subjective opinions, a capability lacking in current models.

Purpose of the Study:

  • To propose and evaluate a new approach for answering why-questions.
  • To enhance the ability of systems to provide explanations for complex queries.
  • To improve the answer rate for why-questions beyond the capabilities of current deep learning models.

Main Methods:

  • Utilizing discourse analysis to identify explicit arguments and opinions.
  • Applying natural language inference to detect implicit arguments and calculate sentence similarity.
  • Combining discourse analysis and natural language inference to generate answer candidates for why-questions.

Main Results:

  • The proposed system demonstrated a higher answer rate (77.0%) compared to a deep learning model (61.0%) on a Vietnamese translated SQuAD v1.1 why-question dataset.
  • While the F1 score did not surpass deep learning models, the system successfully answered a greater number of why-questions.
  • The approach effectively identifies both explicit and implicit information relevant to explaining why-questions.

Conclusions:

  • The integration of discourse analysis and natural language inference offers a promising direction for improving why-question answering.
  • The developed system shows potential in providing more comprehensive answers to explanation-seeking queries.
  • Further research can build upon this hybrid approach to bridge the gap in deep learning model performance for complex question types.