Ameliorating Questions Classification

Abstract

One of the most vital steps in automatic Question Answering systems is question classification, also known as Answer type classification, identification or prediction. Precise and accurate question classification can lead to the elimination of irrelevant candidate answers from the pool of answers available for the question. High accuracy of question classification means accurate answer for the given question. This paper proposes an approach, named as Question Sentence Embedding (QSE), for question classification by utilizing semantic features. Extracting large number of features do not solve the problem every time. Our proposed approach simplifies feature extraction stage by not extracting features such as named entities, present in fewer questions because of their short length, and hypernyms and hyponyms of a word that requires WordNet extension. These features make the system more dependent on external sources. We have used Universal Sentence Embedding with Transformer Encoder for obtaining sentence level embedding vector of fixed size and then calculated the semantic similarity among these vectors to classify questions in their predefined categories. As it is the time of global pandemic COVID-19 and people are more curios to ask questions about COVID-19. Our experimental dataset is publicly available COVID-Q dataset. Our results have achieved an accuracy of 69% on COVID-19 question classification task. Our proposed approach has outperformed the baseline method, 53.4%, manifesting the efficacy of proposed QSE method.