Abstract
Machine reading comprehension (MRC) is a typical natural language processing (NLP) task and has developed rapidly in the last few years. Various reading comprehension datasets have been built to support MRC studies. However, large-scale and high-quality datasets are rare due to the high complexity and huge workforce cost of making such a dataset. Besides, most reading comprehension datasets are in English, and Chinese datasets are insufficient. In this paper, we propose an automatic method for MRC dataset generation, and build the largest Chinese medical reading comprehension dataset presently named CMedRC. Our dataset contains 17k questions generated by our automatic method and some seed questions. We obtain the corresponding answers from a medical knowledge graph and manually check all of them. Finally, we test BiLSTM and BERT-based pre-trained language models (PLMs) on our dataset and propose a baseline for the following studies. Results show that the automatic MRC dataset generation method is considerable for future model improvements.
The work is supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61806111, NSFC for Distinguished Young Scholar under Grant No. 61825602 and National Key R&D Program of China under Grant No. 2020AAA010520002.
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References
Cui, Y., Chen, Z., Wei, S., Wang, S., Liu, T., Hu, G.: Attention-over-attention neural networks for reading comprehension. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 593–602. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/P17-1055. http://aclweb.org/anthology/P17-1055
Cui, Y., et al.: A span-extraction dataset for Chinese machine reading comprehension, p. 7
Cui, Y., Liu, T., Chen, Z., Wang, S., Hu, G.: Consensus attention-based neural networks for Chinese reading comprehension. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1777–1786. The COLING 2016 Organizing Committee (2016). http://aclweb.org/anthology/C16-1167
Dhingra, B., Liu, H., Yang, Z., Cohen, W., Salakhutdinov, R.: Gated-attention readers for text comprehension. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1832–1846. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/P17-1168. http://www.aclweb.org/anthology/P17-1168
He, W., et al.: DuReader: a Chinese machine reading comprehension dataset from real-world applications. arXiv preprint arXiv:1711.05073 (2017)
Hill, F., Bordes, A., Chopra, S., Weston, J.: The goldilocks principle: reading children’s books with explicit memory representations. arXiv preprint arXiv:1511.02301 (2015)
Hu, M., Peng, Y., Huang, Z., Qiu, X., Wei, F., Zhou, M.: Reinforced mnemonic reader for machine reading comprehension. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 4099–4106. International Joint Conferences on Artificial Intelligence Organization, July 2018. https://doi.org/10.24963/ijcai.2018/570
Joshi, M., Choi, E., Weld, D., Zettlemoyer, L.: TriviaQA: a large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551 (2018)
Kadlec, R., Schmid, M., Bajgar, O., Kleindienst, J.: Text understanding with the attention sum reader network. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 908–918. Association for Computational Linguistics (2016). https://doi.org/10.18653/v1/P16-1086. http://aclweb.org/anthology/P16-1086
Lai, G., Xie, Q., Liu, H., Yang, Y., Hovy, E.: RACE: large-scale reading comprehension dataset from examinations. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 785–794. Association for Computational Linguistics (2017). http://aclweb.org/anthology/D17-1082
Liu, T., Cui, Y., Yin, Q., Zhang, W.N., Wang, S., Hu, G.: Generating and exploiting large-scale pseudo training data for zero pronoun resolution. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 102–111. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/P17-1010. http://www.aclweb.org/anthology/P17-1010
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2383–2392. Association for Computational Linguistics (2016). https://doi.org/10.18653/v1/D16-1264. http://www.aclweb.org/anthology/D16-1264
Reddy, S., Chen, D., Manning, C.: CoQA: a conversational question answering challenge. arXiv preprint arXiv:1808.07042 (2018)
Sun, K., Yu, D., Yu, D., Cardie, C.: Investigating prior knowledge for challenging Chinese machine reading comprehension. Trans. Assoc. Comput. Linguist. 8, 141–155 (2020)
Sun, Y., et al.: ERNIE: enhanced representation through knowledge integration. arXiv:1904.09223 [cs], April 2019. http://arxiv.org/abs/1904.09223. arXiv: 1904.09223
Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., Suleman, K.: NewsQA: a machine comprehension dataset. arXiv preprint arXiv:1611.09830 (2016)
Wang, S., Jiang, J.: Machine comprehension using match-LSTM and answer pointer. arXiv preprint arXiv:1608.07905 (2016)
Wang, S., Jiang, J.: Machine Comprehension Using Match-LSTM and Answer Pointer. arXiv:1608.07905 [cs], November 2016. http://arxiv.org/abs/1608.07905. arXiv: 1608.07905
Wang, W., Yan, M., Wu, C.: Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1705–1714. Association for Computational Linguistics (2018). http://aclweb.org/anthology/P18-1158
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. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/P17-1018. http://www.aclweb.org/anthology/P17-1018
Xiong, C., Zhong, V., Socher, R.: Dynamic coattention networks for question answering. arXiv preprint arXiv:1611.01604 (2016)
Yu, A.W., et al.: QANet: combining local convolution with global self-attention for reading comprehension. arXiv preprint arXiv:1804.09541 (2018)
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Zhao, H. et al. (2021). A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph. In: Li, S., et al. Chinese Computational Linguistics. CCL 2021. Lecture Notes in Computer Science(), vol 12869. Springer, Cham. https://doi.org/10.1007/978-3-030-84186-7_18
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