Abstract

Question Generation (QG) is the task of generating questions from a given passage. One of the key requirements of QG is to generate a question such that it results in a target answer. Previous works used a target answer to obtain a desired question. However, we also want to specify how to ask questions and improve the quality of generated questions. In this study, we explore the use of interrogative phrases as additional sources to control QG. By providing interrogative phrases, we expect that QG can generate a more reliable sequence of words subsequent to an interrogative phrase. We present a baseline sequence-to-sequence model with the attention, copy, and coverage mechanisms, and show that the simple baseline achieves state-of-the-art performance. The experiments demonstrate that interrogative phrases contribute to improving the performance of QG. In addition, we report the superiority of using interrogative phrases in human evaluation. Finally, we show that a question answering system can provide target answers more correctly when the questions are generated with interrogative phrases.

Highlights

  • Question Generation (QG) is the task of generating questions from a given passage

  • The sentence “Bob went to the airport yesterday.” can have various candidate questions such as “When did Bob go to the airport?,” “Where did Bob go yesterday?,” and “Who went to the airport yesterday?” It is necessary for QG to specify a desired question from among multiple possibilities

  • We explore the use of interrogative phrases as additional sources to control QG

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Summary

Introduction

Question Generation (QG) is the task of generating questions from a given passage. It has several applications: (1) In the area of the education, QG can help to generate questions for reading comprehension materials (Heilman and Smith, 2010). (2) QG can aid development of conversational chatbots, which ask questions (Mostafazadeh et al, 2016). (3) QG is useful for development of question answering datasets (Duan et al, 2017; Tang et al, 2018).One of the key requirements of QG is to generate a question such that it asks a target answer. Question Generation (QG) is the task of generating questions from a given passage. (2) QG can aid development of conversational chatbots, which ask questions (Mostafazadeh et al, 2016). (3) QG is useful for development of question answering datasets (Duan et al, 2017; Tang et al, 2018). One of the key requirements of QG is to generate a question such that it asks a target answer. Only one question is generated from multiple possibilities at random. Most recent studies tried to generate a desired question using a target answer as the input. Kim et al (2018) separated target answer words from the original passages to address the problem of many generated questions including the target answer words

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