Abstract
This paper focuses on automatic question generation (QG) that transforms a narrative sentence into an interrogative sentence. Recently, neural networks have been used in this task due to its extraordinary ability of semantics encoding and decoding. We propose an approach which incorporates semantics of the possible question type. We utilize the Convolutional Neural Network (CNN) for predicting question type of the answer phrases in the narrative sentence. In order to incorporate the question type semantics into the generating process, we classify the question type which the answer phrases refer to. In addition, We use Bidirectional Long Short Term Memory (Bi-LSTM) to construct the question generating model. The experiment results show that our method outperforms the baseline system with the improvement of 1.7% on BLEU-4 score and beyonds the state-of-the-art.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G., (eds.) Proceedings of COMPSTAT 2010, pp. 177–186. Springer (2010). https://doi.org/10.1007/978-3-7908-2604-3_16
Chen, X., Fang, H., Lin, T.Y., Vedantam, R., Gupta, S., Dollár, P., Zitnick, C.L.: Microsoft coco captions: data collection and evaluation server. arXiv preprint arXiv:1504.00325 (2015)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(Aug), 2493–2537 (2011)
De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Annal. Oper. Res. 134(1), 19–67 (2005)
Denkowski, M., Lavie, A.: Meteor universal: Language specific translation evaluation for any target language. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 376–380 (2014)
Du, X., Shao, J., Cardie, C.: Learning to ask: Neural question generation for reading comprehension. arXiv preprint arXiv:1705.00106 (2017)
Duan, N., Tang, D., Chen, P., Zhou, M.: Question generation for question answering. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 866–874 (2017)
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)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. Text Summarization Branches Out (2004)
Liu, M., Rus, V., Liu, L.: Automatic chinese factual question generation. IEEE Trans. Learn. Technol. 10(2), 194–204 (2017)
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.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)
Rus, V., Wyse, B., Piwek, P., Lintean, M., Stoyanchev, S., Moldovan, C.: The first question generation shared task evaluation challenge. In: Proceedings of the 6th International Natural Language Generation Conference, pp. 251–257. Association for Computational Linguistics (2010)
Schwarz, B., Kirchgässner, W., Landwehr, R.: An optimizer for ADA-design, experiences and results. In: ACM SIGPLAN Notices, vol. 23, pp. 175–184. ACM (1988)
Voorhees, E.M., et al.: The TREC-8 question answering track report. In: Trec, vol. 99, pp. 77–82 (1999)
Zhou, Q., Yang, N., Wei, F., Tan, C., Bao, H., Zhou, M.: Neural question generation from text: a preliminary study. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds.) NLPCC 2017. LNCS (LNAI), vol. 10619, pp. 662–671. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73618-1_56
Acknowledgements
This work was supported by the national Natural Science Foundation of China via Nos. 2017YFB1002104, 61672368 and 61672367.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Dong, X., Hong, Y., Chen, X., Li, W., Zhang, M., Zhu, Q. (2018). Neural Question Generation with Semantics of Question Type. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-99501-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99500-7
Online ISBN: 978-3-319-99501-4
eBook Packages: Computer ScienceComputer Science (R0)
