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2018 | OriginalPaper | Buchkapitel

Neural Question Generation from Text: A Preliminary Study

verfasst von : Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, Ming Zhou

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a preliminary study on neural question generation from text with the SQuAD dataset, and the experiment results show that our method can produce fluent and diverse questions.

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Titel: Neural Question Generation from Text: A Preliminary Study
verfasst von: Qingyu Zhou
Nan Yang
Furu Wei
Chuanqi Tan
Hangbo Bao
Ming Zhou
Verlag: Springer International Publishing
Buch: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-319-73617-4

Electronic ISBN: 978-3-319-73618-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-73618-1_56

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