Abstract

With the swift growth of the information over the past few years, taking full benefit is increasingly essential. Question Answering System is one of the promising methods to access this much information. The Question Answering System lacks humans’ common sense and reasoning power and cannot identify unanswerable questions and irrelevant questions. These questions are answered by making unreliable and incorrect guesses. In this paper, we address this limitation by proposing a Question Similarity mechanism. Before a question is posed to a Question-Answering system, it is compared with possible generated questions of the given paragraph, and then a Question Similarity Score is generated. The Question Similarity mechanism effectively identifies the unanswerable and irrelevant questions. The proposed Question Similarity mechanism incorporates a human way of reasoning to identify unanswerable and irrelevant questions. This mechanism can avoid the unanswerable and irrelevant questions altogether from being posed to the Question Answering system. It helps the Question Answering Systems to focus only on the answerable questions to improve their performance. Along with this, we introduce an application of the Question Answering System that generates the question-answer pairs given a passage and is useful in several fields.

Highlights

  • The Question Answering System (QAS) plays an important role in getting questions and automatically answering them using a knowledge information system

  • Once the question generation system generates the possible set of questions based on the answer spans, which are found by a noun and verb phrases in the passage, the generated questions are given to the question answering system

  • We introduce an application by combining the Question Generation and Question Answering system called automatic question-pairs generation system, where all possible question and answer pairs will be generated

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Summary

Introduction

The Question Answering System (QAS) plays an important role in getting questions and automatically answering them using a knowledge information system. This paper blends the essence of Question Generation, Question Comprehension, and Question Answering to overcome the Question Answering System’s limitations. Question Answering System had existed way back in the 1960s. The first-ever question answering system introduced was BASEBALL [16]. It was built with a sequence of handwritten rules, and all baseball figures were stored in a database accumulated over the year. LUNAR [42] was introduced during the Apollo mission to answer questions. This system was built to answer the moon’s geological patterns and other related information about the APOLLO mission. The customized nature of this system leads to the generation of highly accurate answers

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