Abstract
Scientific Machine Reading Comprehension (SMRC) aims to understand scientific long text by providing answers for the given questions. Most existing methods trend to answer the question using Transformer-based models. However, in the scientific domain, the original text is longer than the general domain. In this paper, we proposed a model that consists of a content retrieval module and a pre-trained model module. The content retrieval module finds the most semantically relevant sentences from the text and re-rank them. The seleted sentences and question will be input into the pre-trained model to get the answers. This model could overcome the length limitation of Transformer model length while achieving impressive results. Our model achieved 0.45 score of RougeL, resulting in the second place in the NLPCC2023 Shared Task2.
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
Chen, D.: Neural Reading Comprehension and Beyond. Stanford University, Stanford (2018)
Hermann, K.M., et al.: Teaching machines to read and comprehend. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Wen, T.H., et al.: A network-based end-to-end trainable task-oriented dialogue system. arXiv preprint arXiv:1604.04562 (2016)
Chen, H., Liu, X., Yin, D., Tang, J.: A survey on dialogue systems: Recent advances and new frontiers. ACM SIGKDD Explor. Newsl. 19(2), 25–35 (2017)
Zhang, X., Zheng, H., Nie, Y., Huang, H., Mao, X.L.: SCIMRC: multi-perspective scientific machine reading comprehension. arXiv preprint arXiv:2306.14149 (2023)
Chen, D., Yih, W.T.: Open-domain question answering. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, pp. 34–37 (2020)
Manning, C.D.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)
Lehnert, W.G.: The Process of Question Answering. Yale University, New Haven (1977)
Hirschman, L., Light, M., Breck, E., Burger, J.D.: Deep read: a reading comprehension system. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pp. 325–332 (1999)
Riloff, E., Thelen, M.: A rule-based question answering system for reading comprehension tests. In: ANLP-NAACL 2000 Workshop: Reading Comprehension Tests as Evaluation for Computer-Based Language Understanding Systems (2000)
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)
Joshi, M., Choi, E., Weld, D.S., Zettlemoyer, L.: TriviaQA: a large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551 (2017)
Kadlec, R., Schmid, M., Bajgar, O., Kleindienst, J.: Text understanding with the attention sum reader network. arXiv preprint arXiv:1603.01547 (2016)
Dhingra, B., Liu, H., Yang, Z., Cohen, W.W., Salakhutdinov, R.: Gated-attention readers for text comprehension. arXiv preprint arXiv:1606.01549 (2016)
Wang, W., Yang, N., Wei, F., Chang, B., Zhou, M.: Gated self-matching networks for reading comprehension and question answering. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 189–198 (2017)
Cui, Y., Chen, Z., Wei, S., Wang, S., Liu, T., Hu, G.: Attention-over-attention neural networks for reading comprehension. arXiv preprint arXiv:1607.04423 (2016)
Seo, M., Kembhavi, A., Farhadi, A., Hajishirzi, H.: Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603 (2016)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485–5551 (2020)
Zhao, J., et al.: ROR: read-over-read for long document machine reading comprehension. arXiv preprint arXiv:2109.04780 (2021)
Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading wikipedia to answer open-domain questions. arXiv preprint arXiv:1704.00051 (2017)
Lee, J., Yun, S., Kim, H., Ko, M., Kang, J.: Ranking paragraphs for improving answer recall in open-domain question answering. arXiv preprint arXiv:1810.00494 (2018)
Wang, S., et al.: R 3: reinforced ranker-reader for open-domain question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Min, S., Chen, D., Zettlemoyer, L., Hajishirzi, H.: Knowledge guided text retrieval and reading for open domain question answering. arXiv preprint arXiv:1911.03868 (2019)
Asai, A., Hashimoto, K., Hajishirzi, H., Socher, R., Xiong, C.: Learning to retrieve reasoning paths over wikipedia graph for question answering. arXiv preprint arXiv:1911.10470 (2019)
Yi, X., et al.: Sampling-bias-corrected neural modeling for large corpus item recommendations. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 269–277 (2019)
Acknowledgment
This work is supported by National Natural Science Foundation of China (Nos. 62066033, 61966025); Inner Mongolia Applied Technology Research and Development Fund Project (Nos. 2019GG372, 2020PT0002, 2022YFDZ0059); Inner Mongolia Natural Science Foundation (2020BS06001). We are grateful for the useful suggestions from the anonymous reviewers.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, J., Wang, W., Shao, S. (2023). Scientific Reading Comprehension with Sentences Selection and Ranking. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14304. Springer, Cham. https://doi.org/10.1007/978-3-031-44699-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-44699-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-44698-6
Online ISBN: 978-3-031-44699-3
eBook Packages: Computer ScienceComputer Science (R0)
