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A question-entailment approach to question answering.

Asma Ben Abacha1, Dina Demner-Fushman2

  • 1Lister Hill Center, U.S. National Library of Medicine, U.S. National Institutes of Health, Bethesda, MD, USA. asma.benabacha@nih.gov.

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|October 24, 2019
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Summary

This study introduces a novel question answering approach using Recognizing Question Entailment (RQE) and information retrieval (IR) for medical questions. The combined method significantly improved answer accuracy, outperforming previous benchmarks.

Keywords:
Consumer Health QuestionsDeep LearningInformation RetrievalMachine LearningMedical Question-Answer DatasetQuestion AnsweringRecognizing Question Entailment

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Area of Science:

  • Computer Science
  • Information Science
  • Medical Informatics

Background:

  • Large-scale information retrieval (IR) faces challenges in domain-specific natural language question answering (QA).
  • Existing QA systems struggle with question understanding and accurate answer extraction.
  • Mapping new questions to previously answered similar questions is a promising QA strategy.

Purpose of the Study:

  • To develop and evaluate a novel QA approach leveraging Recognizing Question Entailment (RQE).
  • To assess the effectiveness of combining IR models with RQE for medical QA.
  • To introduce and share the MedQuAD dataset for medical QA research.

Main Methods:

  • Compared logistic regression and deep learning for RQE across various datasets and domains.
  • Integrated Information Retrieval (IR) models with the best RQE method to select entailed questions and rank answers.
  • Developed and utilized the MedQuAD collection of 47,457 medical question-answer pairs.

Main Results:

  • The proposed QA approach, combining IR and RQE, achieved superior performance on real medical questions.
  • The system demonstrated a 29.8% increase over the best official score in the TREC 2017 LiveQA medical task.
  • Deep learning methods showed strong performance for RQE, particularly when trained on relevant datasets.

Conclusions:

  • Question entailment is highly relevant for improving QA systems.
  • Combining IR and RQE is an effective strategy for advancing QA, especially in specialized domains like medicine.
  • Utilizing a curated set of reliable medical sources significantly enhances QA performance.