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.
Q. Zhou—Contribution during internship at Microsoft Research.
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Notes
- 1.
We re-distribute the processed data split and PCFG-Trans baseline code at http://res.qyzhou.me.
- 2.
- 3.
We treat questions ‘what country’, ‘what place’ and so on as WHERE type questions. Similarly, questions containing ‘what time’, ‘what year’ and so forth are counted as WHEN type questions.
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Zhou, Q., Yang, N., Wei, F., Tan, C., Bao, H., Zhou, M. (2018). Neural Question Generation from Text: A Preliminary Study. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_56
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