Question Classification and Answer Extraction for Developing a Cooking QA System

Abdullah Faiz Ur Rahman Khilji, Riyanka Manna, Sahinur Rahman Laskar, Partha Pakray, Dipankar Das, Sivaji Bandyopadhyay, Alexander Gelbukh


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%.


Question classification, answer extraction, cooking QA, BERT

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