Abstract
Chinese Event Detection (CED) aims to detect events from unstructured sentences. Due to the difficulty of labeling event detection datasets, previous approaches suffer from severe data sparsity problem. To address this issue, we propose a novel Lattice LSTM based multi-task learning model. On one hand, we utilize multi-granularity word information via Lattice LSTM to fully exploit existing datasets. On the other hand, we employ the multi-task learning mechanism to improve CED with datasets from other tasks. Specifically, we combine Name Entity Recognition (NER) and Mask Word Prediction (MWP) as two auxiliary tasks to learn both entity and general language information. Experiments show that our approach outperforms the six SOTA methods by 1.9% on ACE2005 benchmark. The source code is released on https://github.com/tongmeihan1995/MLL-chinese-event-detection.
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
References
Chen, C., Ng, V.: Joint modeling for Chinese event extraction with rich linguistic features. In: COLING, pp. 529–544 (2012)
Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural networks. In: ACL, vol. 1, pp. 167–176 (2015)
Doddington, G.R., Mitchell, A., Przybocki, M.A., Ramshaw, L.A., Strassel, S.M., Weischedel, R.M.: The automatic content extraction (ACE) program-tasks, data, and evaluation. In: LREC (2004)
Feng, X., Qin, B., Liu, T.: A language-independent neural network for event detection. Sci. China Inf. Sci. 61(9), 1–12 (2018). https://doi.org/10.1007/s11432-017-9359-x
Lee, J., et al.: Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234–1240 (2020)
Li, Q., Ji, H., Huang, L.: Joint event extraction via structured prediction with global features. In: ACL (2013)
Liao, S., Grishman, R.: Using document level cross-event inference to improve event extraction. In: ACL, pp. 789–797 (2010)
Lin, H., Lu, Y., Han, X., Sun, L.: Nugget proposal networks for Chinese event detection. arXiv preprint arXiv:1805.00249 (2018)
Lin, H., Lu, Y., Han, X., Sun, L.: Cost-sensitive regularization for label confusion-aware event detection. arXiv preprint arXiv:1906.06003 (2019)
Liu, J., Chen, Y., Liu, K.: Exploiting the ground-truth: an adversarial imitation based knowledge distillation approach for event detection. In: AAAI, pp. 6754–6761 (2019)
Liu, S., Chen, Y., He, S., Liu, K., Zhao, J.: Leveraging framenet to improve automatic event detection. In: ACL, vol. 1, pp. 2134–2143 (2016)
Lu, W., Nguyen, T.H.: Similar but not the same: word sense disambiguation improves event detection via neural representation matching. In: EMNLP (2018)
Makarov, P., Clematide, S.: UZH at TAC KBP 2017: event nugget detection via joint learning with softmax-margin objective. In: TAC (2017)
McClosky, D., Surdeanu, M., Manning, C.D.: Event extraction as dependency parsing. In: ACL, pp. 1626–1635 (2011)
Miwa, M., Sætre, R., Kim, J.D., Tsujii, J.: Event extraction with complex event classification using rich features. J. Bioinform. Comput. Biol. 8(01), 131–146 (2010)
Rospocher, M., et al.: Building event-centric knowledge graphs from news. J. Web Semant. 37, 132–151 (2016)
Wang, X., et al.: Cross-type biomedical named entity recognition with deep multi-task learning. arXiv preprint arXiv:1801.09851 (2018)
Yang, T.H., Huang, H.H., Yen, A.Z., Chen, H.H.: Transfer of frames from English framenet to construct Chinese framenet: a bilingual corpus-based approach. In: LREC (2018)
Zaremoodi, P., Buntine, W., Haffari, G.: Adaptive knowledge sharing in multi-task learning: improving low-resource neural machine translation. In: ACL (2018)
Zeng, Y., et al.: Scale up event extraction learning via automatic training data generation. In: AAAI (2018)
Zeng, Y., Yang, H., Feng, Y., Wang, Z., Zhao, D.: A convolution BiLSTM neural network model for Chinese event extraction. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC -2016. LNCS (LNAI), vol. 10102, pp. 275–287. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_23
Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. arXiv preprint arXiv:1805.02023 (2018). https://arxiv.org/pdf/1805.02023
Zhao, Y., Jin, X., Wang, Y., Cheng, X.: Document embedding enhanced event detection with hierarchical and supervised attention. In: ACL, vol. 2, pp. 414–419 (2018)
Acknowledgments
This work is supported by the National Key Research and Development Program of China (2018YFB1005100 and 2018YFB1005101), NSFC key projects (U1736204, 61533018), and grants from Beijing Academy of Artificial Intelligence (BAAI2019ZD0502) and the Institute for Guo Qiang, Tsinghua University (2019GQB0003). It also got partial support from National Engineering Laboratory for Cyberlearning and Intelligent Technology, and Beijing Key Lab of Networked Multimedia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tong, M., Xu, B., Wang, S., Hou, L., Li, J. (2020). Improving Low-Resource Chinese Event Detection with Multi-task Learning. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12274. Springer, Cham. https://doi.org/10.1007/978-3-030-55130-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-55130-8_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55129-2
Online ISBN: 978-3-030-55130-8
eBook Packages: Computer ScienceComputer Science (R0)