Question Classification and Answer Extraction for Developing a Cooking QA System

Authors

  • Abdullah Faiz Ur Rahman Khilji National Institute of Technology Silchar, Department of Computer Science and Engineering
  • Riyanka Manna Jadavpur University, Department of Computer Science and Engineering
  • Sahinur Rahman Laskar Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Partha Pakray National Institute of Technology Silchar, Department of Computer Science and Engineering
  • Dipankar Das Jadavpur University, Department of Computer Science and Engineering
  • Sivaji Bandyopadhyay National Institute of Technology Silchar, Department of Computer Science and Engineering
  • Alexander Gelbukh Instituto Politécnico Nacional, Centro de Investigación en Computación

DOI:

https://doi.org/10.13053/cys-24-2-3445

Keywords:

Question classification, answer extraction, cooking QA, BERT

Abstract

In an automated Question Answering (QA) system, Question Classification (QC) is an essential module. The aim of QC is to identify the type of questions and classify them based on the expected answer type. Although the machine-learning approach overcomes the limitation of rules as is the case with the conventional rule-based approach but is restricted to the predefined class of questions. The existing approaches are too specific for the users. To address this challenge, we have developed a cooking QA system in which a recipe question is contextually classified into a particular category using deep learning techniques. The question class is then used to extract the requisite details from the recipe obtained via the rule-based approach to provide a precise answer. The main contribution of this paper is the description of the QC module of the cooking QA system. The obtained intermediate classification accuracy over the unseen data is 90% and the human evaluation accuracy of the final system output is 39.33%.

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Published

2020-06-26

Issue

Vol. 24 No. 2 (2020): Thematic Issue on Language & Knowledge Engineering (Guest editors: D. Pinto, B. Beltrán, A. Vázquez)

Section

Articles

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