Abstract
Neural network-based methods represent the state-of-the-art in question generation from text. Existing work focuses on generating only questions from text without concerning itself with answer generation. Moreover, our analysis shows that handling rare words and generating the most appropriate question given a candidate answer are still challenges facing existing approaches. We present a novel two-stage process to generate question-answer pairs from the text. For the first stage, we present alternatives for encoding the span of the pivotal answer in the sentence using Pointer Networks. In our second stage, we employ sequence to sequence models for question generation, enhanced with rich linguistic features. Finally, global attention and answer encoding are used for generating the question most relevant to the answer. We motivate and linguistically analyze the role of each component in our framework and consider compositions of these. This analysis is supported by extensive experimental evaluations. Using standard evaluation metrics as well as human evaluations, our experimental results validate the significant improvement in the quality of questions generated by our framework over the state-of-the-art. The technique presented here represents another step towards more automated reading comprehension assessment. We also present a live system (Demo of the system is available at https://www.cse.iitb.ac.in/~vishwajeet/autoqg.html.) to demonstrate the effectiveness of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)
Copestake, A., Flickinger, D., Sag, I.A., Pollard, C.: Minimal recursion semantics: an introduction (1999). http://www-csli.stanford.edu/~sag/sag.html, draft
Denkowski, M., Lavie, A.: Meteor universal: Language specific translation evaluation for any target language. In: Proceedings of the EACL 2014 Workshop on Statistical Machine Translation (2014)
Du, X., Shao, J., Cardie, C.: Learning to ask: neural question generation for reading comprehension. In: Proceedings of the 55th Annual Meeting of the ACL (Volume 1: Long Papers), vol. 1, pp. 1342–1352 (2017)
Eriguchi, A., Hashimoto, K., Tsuruoka, Y.: Tree-to-sequence attentional neural machine translation. CoRR abs/1603.06075 (2016). http://arxiv.org/abs/1603.06075
Heilman, M., Smith, N.A.: Good question! statistical ranking for question generation. In: Human Language Technologies: the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 609–617. Association for Computational Linguistics (2010)
Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Marie-Francine Moens, S.S. (ed.) Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, pp. 74–81. Association for Computational Linguistics, Barcelona, July 2004. http://www.aclweb.org/anthology/W04-1013
Luong, M., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. CoRR abs/1508.04025 (2015). http://arxiv.org/abs/1508.04025
Mannem, P., Prasad, R., Joshi, A.: Question generation from paragraphs at UPenn: QGSTEC system description. In: Proceedings of QG2010: The Third Workshop on Question Generation, pp. 84–91 (2010)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). http://www.aclweb.org/anthology/D14-1162
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)
Sennrich, R., Haddow, B.: Linguistic input features improve neural machine translation. CoRR abs/1606.02892 (2016). http://arxiv.org/abs/1606.02892
Serban, I.V., GarcÃa-Durán, A., Gulcehre, C., Ahn, S., Chandar, S., Courville, A., Bengio, Y.: Generating factoid questions with recurrent neural networks: the 30m factoid question-answer corpus. arXiv preprint arXiv:1603.06807 (2016)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: NIPS, pp. 2692–2700 (2015)
Yao, X., Bouma, G., Zhang, Y.: Semantics-based question generation and implementation. Dialogue Discourse Spec. Issue Question Gener. 3(2), 11–42 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Kumar, V., Boorla, K., Meena, Y., Ramakrishnan, G., Li, YF. (2018). Automating Reading Comprehension by Generating Question and Answer Pairs. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-93040-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93039-8
Online ISBN: 978-3-319-93040-4
eBook Packages: Computer ScienceComputer Science (R0)