Alignment-Based Graph Network for Judicial Examination Task

1. Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., Sima’an, K.: Graph convolutional encoders for syntax-aware neural machine translation. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1957–1967 (2017)

   Google Scholar 

2. Chen, Y., Wu, L., Zaki, M.J.: Reinforcement learning based graph-to-sequence model for natural question generation. In: 8th International Conference on Learning Representations (2020)

   Google Scholar 

3. Cui, Y., Che, W., Liu, T., Qin, B., Wang, S., Hu, G.: Revisiting pre-trained models for Chinese natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pp. 657–668 (2020)

   Google Scholar 

4. 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, vol. 1, pp. 593–602 (2017)

   Google Scholar 

5. Dai, W., Qiu, L., Wu, A., Qiu, M.: Cloud infrastructure resource allocation for big data applications. IEEE Trans. Big Data 4(3), 313–324 (2018)

   Article  Google Scholar 

6. De Cao, N., Aziz, W., Titov, I.: Question answering by reasoning across documents with graph convolutional networks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 2306–2317 (2019)

   Google Scholar 

7. 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)

8. Gai, K., Qiu, M.: Reinforcement learning-based content-centric services in mobile sensing. IEEE Network 32(4), 34–39 (2018)

   Article  Google Scholar 

9. Gai, K., Qiu, M., Zhao, H., Sun, X.: Resource management in sustainable cyber-physical systems using heterogeneous cloud computing. IEEE Trans. Sustain. Comput. 3(2), 60–72 (2018)

   Article  Google Scholar 

10. Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 729–734 (2005)

    Google Scholar 

11. He, W., et al.: Dureader: a chinese machine reading comprehension dataset from real-world applications. arXiv preprint arXiv:1711.05073 (2017)

12. Hermann, K.M., et al.: Teaching machines to read and comprehend In: Proceedings of the 28th International Conference on Neural Information Processing Systems, vol. 1, pp. 1693–1701 (2015)

    Google Scholar 

13. Ji, L., Wei, Z., Hu, X., Liu, Y., Zhang, Q., Huang, X.-J.: Incorporating argument-level interactions for persuasion comments evaluation using co-attention model. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 3703–3714 (2018)

    Google Scholar 

14. 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 (2017)

    Google Scholar 

15. Liu, Y., Luo, X., Yang, X.: Semantics and structure based recommendation of similar legal cases. In: 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 388–395 (2019)

    Google Scholar 

16. Liu, Z., Xiong, C., Sun, M., Liu, Z.: Fine-grained fact verification with kernel graph attention network. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7342–7351 (2020)

    Google Scholar 

17. Marcheggiani, D., Titov, I.: Encoding sentences with graph convolutional networks for semantic role labeling. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1506–1515 (2017)

    Google Scholar 

18. Mihaylov, T., Frank, A.: Knowledgeable reader: enhancing cloze-style reading comprehension with external commonsense knowledge. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 821–832 (2018)

    Google Scholar 

19. 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 (2016)

    Google Scholar 

20. Seo, M., Kembhavi, A., Farhadi, A., Hajishirzi, H.: Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603 (2016)

21. Sordoni, A., Bachman, P., Trischler, A., Bengio, Y.: Iterative alternating neural attention for machine reading. arXiv preprint arXiv:1606.02245 (2016)

22. Sun, H., Dhingra, B., Zaheer, M., Mazaitis, K., Salakhutdinov, R., Cohen, W.: Open domain question answering using early fusion of knowledge bases and text. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4231–4242 (2018)

    Google Scholar 

23. Vaswani, A.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000–6010. Curran Associates Inc. (2017)

    Google Scholar 

24. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: Proceedings of the 6th International Conference on Learning Representations, pp. 1–12 (2018)

    Google Scholar 

25. Wang, S., Jiang, J.: Machine comprehension using match-LSTM and answer pointer. arXiv preprint arXiv:1608.07905 (2016)

26. Wang, S., Yu, M., Jiang, J., Chang, S.: A co-matching model for multi-choice reading comprehension. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 746–751 (2018)

    Google Scholar 

27. 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, vol. 1, pp. 189–198 (2017)

    Google Scholar 

28. Wang, W., Yang, N., Wei, F., Chang, B., Zhou, M.: R-NET: machine reading comprehension with self-matching networks. Tech. Rep. Nat. Lang. Comput. Group, Microsoft, Asia, Beijing, China, 5 (2017)

    Google Scholar 

29. Wu, J., Liu, J., Luo, X.: Few-shot legal knowledge question answering system for COVID-19 epidemic. In: 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence, pp. 1–6 (2020)

    Google Scholar 

30. Xu, K., Wu, L., Wang, Z., Feng, Y., Sheinin, V.: Sql-to-text generation with graph-to-sequence model. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 931–936, (2018)

    Google Scholar 

31. Xu, K., Wu, L., Wang, Z., Feng, Y., Witbrock, M., Sheinin, V.: Graph2seq: graph to sequence learning with attention-based neural networks. arXiv preprint arXiv:1804.00823 (2018)

32. Yang, B., Mitchell, T.: Leveraging knowledge bases in LSTMs for improving machine reading. arXiv preprint arXiv:1902.09091 (2019)

33. Yin, W., Ebert, S., Schütze, H.: Attention-based convolutional neural network for machine comprehension. In: Proceedings of the Workshop on Human-Computer Question Answering, pp. 15–21 (2016)

    Google Scholar 

34. Zhang, Y., Qi, P., Manning, C.D.: Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2205–2215. Association for Computational Linguistics (2018)

    Google Scholar 

35. Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., Sun, M.: JEC-QA: a legal-domain question answering dataset. arXiv preprint arXiv:1911.12011 (2019)

36. Zhou, J., et al.:.Gear: graph-based evidence aggregating and reasoning for fact verification. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 892–901 (2019)

    Google Scholar 

37. Zhu, H., Wei, F., Qin, B., Liu, T.: Hierarchical attention flow for multiple-choice reading comprehension. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 6077–6085 (2018)

    Google Scholar 

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