Abstract
NT (Naming-telling) Clause Complex Framework defines the clause complex structures through component sharing and logic-semantic relationships. In this paper, we formalize component sharing recognition as a multi-span extraction problem in machine learning. And we propose a model with mask strategy to recognize the shared components of cross punctuation clauses based on pre-training models. Furthermore, we present a Chinese Long-distance Shared Component Recognition Dataset (LSCR) with four domains, including 43k texts and 156k shared components that need to be predicted. Experimental results and analysis show that our model outperforms previous methods in large margin. All the codes and dataset are available at https://github.com/smiletm/LSCR.
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References
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning (2016)
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. Association for Computational Linguistics (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of Deep Bidirectional Transformers for Language Understanding (2019)
Dong, L., et al.: Unified language model pre-training for natural language understanding and generation (2019)
Ge, S., Song, R.: The naming sharing structure and its cognitive meaning in chinese and english. In: Proceedings of the 2nd Workshop on Semantics-Driven Machine Translation (SedMT 2016) (2016)
Jiang, Y., Song, R.: Topic clause identification based on generalized topic theory. J. Chin. Inf. Process. 26, 114–119 (2012)
Jiang, Y., Song, R.: Topic structure identification of pclause sequence based on generalized topic theory. In: Zhou, M., Zhou, G., Zhao, D., Liu, Q., Zou, L. (eds.) NLPCC 2012. CCIS, vol. 333, pp. 85–96. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34456-5_9
Jiang, Y., Song, R.: Optimization of candidate topic clause evaluation function in topic clause identification (2014)
Jiang, Y., Song, R.: Topic clause identification method based on specific features. J. Comput. Appl. 34, 1345–1349 (2014)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization (2019)
Lu, D.: A study of syntactic constituent and semantic role of new branch topic. In: Proceedings of the 19th Chinese National Conference on Computational Linguistics. Chinese Information Processing Society of China (2020)
Lu, D., Song, R., Shang, Y.: Cognitive complexity of topic in chinese text based on generalized topic structure theory (2014)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training, p. 12 (2018)
Song, R.: Stream model of generalized topic structure in chinese text (2013)
Song, R.: Chinese clause complex and the naming structure (2018)
Song, R., Jiang, Y., Wang, J.: On generalized-topic-based chinese discourse structure (2010)
Mao, T., Zhang, Y., Jiang, Y., Zhang, Y.: Research on construction method of chinese nt clause based on attention-LSTM. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2018. LNCS (LNAI), vol. 11109, pp. 340–350. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99501-4_30
Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (2020)
Zaheer, M., et al.: Big bird: transformers for longer sequences (2020)
Acknowledgements
We would like to thank the anonymous reviewers for their valuable comments. Thanks to all the members who participated in this project, especially the annotators of CCCB. This work is supported by the National Natural Science Foundation of China (No. 62076037).
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Liu, X. et al. (2021). Shared Component Cross Punctuation Clauses Recognition in Chinese. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_57
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DOI: https://doi.org/10.1007/978-3-030-88480-2_57
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