Abstract
In relation extraction, directly adopting a model trained in the source domain to the target domain will suffer greatly performance decrease. Existing studies extract the shared features between domains in a coarse-grained way, which inevitably introduce some domain-specific features or suffer from information loss. Inspired by human beings often using different views to find connection between domains, we argue that, there exist some fine-grained features which can be shared across different views of origin data. In this paper, we proposed a cross-view adaptation network, which use adversarial method to extract shared features and introduce cross-view training to fine-turn it. Besides, we construct some novel views of input data for cross-domain relation extraction. Through experiments we demonstrated that the different views of data we construct can effectively avoid introducing some domain-specific features into unified feature space and help the model learn a fine-grained shared features of different domain. On the three different domains of ACE 2005 dataset, Our method achieved the state-of-the-art results in F1-score.
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References
Blitzer, J., McDonald, R., Pereira, F.: Domain adaptation with structural correspondence learning. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP 2006, Stroudsburg, pp. 120–128. Association for Computational Linguistics (2006)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, pp. 92–100. ACM, New York (1998). https://doi.org/10.1145/279943.279962
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: Domain separation networks. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 343–351. Curran Associates Inc., New York (2016)
Bunescu, R.C., Mooney, R.J.: A shortest path dependency kernel for relation extraction, January 2005. https://doi.org/10.3115/1220575.1220666
Chen, X., Shi, Z., Qiu, X., Huang, X.: Adversarial multi-criteria learning for Chinese word segmentation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, July 2017, pp. 1193–1203. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/P17-1110
Clark, K., Luong, T., Manning, C.D., Le, Q.V.: Semi-supervised sequence modeling with cross-view training (2018)
Fu, L., Nguyen, T.H., Min, B., Grishman, R.: Domain adaptation for relation extraction with domain adversarial neural network. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Taipei, November 2017, pp. 425–429. Asian Federation of Natural Language Processing (2017)
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, vol. 37, pp. 1180–1189. JMLR.org (2015)
Gormley, M.R., Yu, M., Dredze, M.: Improved relation extraction with feature-rich compositional embedding models. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, September 2015, pp. 1774–1784. Association for Computational Linguistics (2015). https://doi.org/10.18653/v1/D15-1205
Jiang, J., Zhai, C.: Instance weighting for domain adaptation in NLP, January 2007
Liu, P., Qiu, X., Huang, X.: Adversarial multi-task learning for text classification. arXiv preprint arXiv:1704.05742 (2017)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of Workshop at ICLR 2013, January 2013
Nguyen, T.H., Grishman, R.: Combining neural networks and log-linear models to improve relation extraction, November 2015. arXiv e-prints
Nguyen, T.H., Grishman, R.: Employing word representations and regularization for domain adaptation of relation extraction. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, June 2014, pp. 68–74. Association for Computational Linguistics (2014). https://doi.org/10.3115/v1/P14-2012
Plank, B., Moschitti, A.: Embedding semantic similarity in tree kernels for domain adaptation of relation extraction, vol. 1, pp. 1498–1507, August 2013
Rios, A., Kavuluru, R., Lu, Z.: Generalizing biomedical relation classification with neural adversarial domain adaptation. Bioinformatics 34(17), 2973–2981 (2018)
Shi, G., et al.: Genre separation network with adversarial training for cross-genre relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, October–November 2018, pp. 1018–1023. Association for Computational Linguistics (2018)
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. CoRR abs/1304.5634 (2013)
Yu, M., Gormley, M.R., Dredze, M.: Combining word embeddings and feature embeddings for fine-grained relation extraction, pp. 1374–1379, January 2015. https://doi.org/10.3115/v1/N15-1155
Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, September 2015, pp. 1753–1762. Association for Computational Linguistics (2015). https://doi.org/10.18653/v1/D15-1203
Acknowledgements
This work was supported by National Key R&D Program of China (2017YFB0802703) and National Natural Science Foundation of China (61602052).
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Yan, B., Zhang, D., Wang, H., Wu, C. (2019). Cross-View Adaptation Network for Cross-Domain Relation Extraction. In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics. CCL 2019. Lecture Notes in Computer Science(), vol 11856. Springer, Cham. https://doi.org/10.1007/978-3-030-32381-3_25
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