Learning to Ask: Neural Question Generation for Reading Comprehension

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1342–1352 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1123 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1342–1352 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1123 Learning to Ask: Neural Question Generation for Reading Comprehension Xinya Du1 Junru Shao2 Claire Cardie1 1Department of Computer Science, Cornell University 2Zhiyuan College, Shanghai Jiao Tong University {xdu, cardie}@cs.cornell.edu yz_sjr@sjtu.edu.cn Abstract We study automatic question generation for sentences from text passages in read- ing comprehension. We introduce an attention-based sequence learning model for the task and investigate the effect of en- coding sentence- vs. paragraph-level infor- mation. In contrast to all previous work, our model does not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead trainable end-to-end via sequence- to-sequence learning. Automatic evalu- ation results show that our system sig- nificantly outperforms the state-of-the-art rule-based system. In human evaluations, questions generated by our system are also rated as being more natural (i.e., grammat- icality, fluency) and as more difficult to an- swer (in terms of syntactic and lexical di- vergence from the original text and reason- ing needed to answer). 1 Introduction Question generation (QG) aims to create natu- ral questions from a given a sentence or para- graph. One key application of question generation is in the area of education — to generate ques- tions for reading comprehension materials (Heil- man and Smith, 2010). Figure 1, for example, shows three manually generated questions that test a user’s understanding of the associated text pas- sage. Question generation systems can also be de- ployed as chatbot components (e.g., asking ques- tions to start a conversation or to request feed- back (Mostafazadeh et al., 2016)) or, arguably, as a clinical tool for evaluating or improving mental health (Weizenbaum, 1966; Colby et al., 1971). In addition to the above applications, question generation systems can aid in the development of Sentence: Oxygen is used in cellular respiration and re- leased by photosynthesis, which uses the en- ergy of sunlight to produce oxygen from water. Questions: – What life process produces oxygen in the presence of light? photosynthesis – Photosynthesis uses which energy to form oxygen from water? sunlight – From what does photosynthesis get oxygen? water Figure 1: Sample sentence from the second para- graph of the article Oxygen, along with the natural questions and their answers. annotated data sets for natural language process- ing (NLP) research in reading comprehension and question answering. Indeed the creation of such datasets, e.g., SQuAD (Rajpurkar et al., 2016) and MS MARCO (Nguyen et al., 2016), has spurred research in these areas. For the most part, question generation has been tackled in the past via rule-based approaches (e.g., Mitkov and Ha (2003); Rus et al. (2010). The success of these approaches hinges criti- cally on the existence of well-designed rules for declarative-to-interrogative sentence transforma- tion, typically based on …

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