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Process."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>Syntactic processing is fundamental to natural language processing. It provides rich and comprehensive syntax information in sentences that could be potentially beneficial for downstream tasks. Recently, pretrained language models have shown great success in Chinese syntactic processing, which typically involves word segmentation, POS tagging, and dependency parsing. However, the on-going research never ends since performance would be degraded drastically when tested on a highly-discrepant domain. This problem is widely accepted as domain adaptation, where the test domain differs from the training domain in supervised learning. Self-training is one promising solution for it, and straightforward source-to-target adaptation has already shown remarkable effectiveness in previous work. While this strategy ignores the fact that sentences of the target domain sentences may have very different gaps from the source training domain. More specifically, sentences with large gaps might fail by direct self-training adaptation. To this end, we propose fine-grained domain adaptation for Chinese syntactic processing in this work, aiming to model the gaps between the source and the target domains accurately and progressively. The key idea is to divide the target domain into fine-grained subdomains by using a specified domain distance metric, and then perform gradual self-training on the subdomains. We further offer an intuitive theoretical illustration based on the theory of Kumar et\u00a0al. (2020) approximately. In addition, a novel representation learning framework is proposed to encode fine-grained subdomains effectively, aiming to utilize the above idea fully. Experimental results on benchmark datasets show that our method can achieve significant improvements over a variety of baselines.<\/jats:p>","DOI":"10.1145\/3629519","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T21:44:48Z","timestamp":1697838288000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Fine-Grained Domain Adaptation for Chinese Syntactic Processing"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6335-1340","authenticated-orcid":false,"given":"Meishan","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Computing and Intelligence, Harbin Institute of Technology (Shenzhen), China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0890-2290","authenticated-orcid":false,"given":"Peiming","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8780-6776","authenticated-orcid":false,"given":"Peijie","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of New Media and Communication, Tianjin University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6570-9406","authenticated-orcid":false,"given":"Dingkun","family":"Long","sequence":"additional","affiliation":[{"name":"National Coalition of Independent Scholars, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4569-8193","authenticated-orcid":false,"given":"Yueheng","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Computing and Intelligence, Tianjin University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8472-5572","authenticated-orcid":false,"given":"Guangwei","family":"Xu","sequence":"additional","affiliation":[{"name":"National Coalition of Independent Scholars, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8412-359X","authenticated-orcid":false,"given":"Pengjun","family":"Xie","sequence":"additional","affiliation":[{"name":"National Coalition of Independent Scholars, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3895-5510","authenticated-orcid":false,"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Computing and Intelligence, Harbin Institute of Technology (Shenzhen), China"}]}],"member":"320","published-online":{"date-parts":[[2023,11,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Shai Ben-David John Blitzer Koby Crammer and Fernando Pereira. 2006. 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In Proceedings of the EMNLP. 2411\u20132420."},{"issue":"10","key":"e_1_3_2_18_2","first-page":"1909","article-title":"Cross-domain sentiment encoding through stochastic word embedding","volume":"32","author":"Hao Yanbin","year":"2019","unstructured":"Yanbin Hao, Tingting Mu, Richang Hong, Meng Wang, Xueliang Liu, and John Y. Goulermas. 2019. Cross-domain sentiment encoding through stochastic word embedding. IEEE Transactions on Knowledge and Data Engineering 32, 10 (2019), 1909\u20131922.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.5715\/jnlp.27.573"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaa8685"},{"key":"e_1_3_2_21_2","first-page":"1989","volume-title":"Proceedings of the ICML","author":"Hoffman Judy","year":"2018","unstructured":"Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell. 2018. Cycada: Cycle-consistent adversarial domain adaptation. In Proceedings of the ICML. 1989\u20131998."},{"key":"e_1_3_2_22_2","first-page":"2790","volume-title":"Proceedings of the ICML","author":"Houlsby Neil","year":"2019","unstructured":"Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In Proceedings of the ICML. 2790\u20132799."},{"key":"e_1_3_2_23_2","first-page":"184","volume-title":"Proceedings of the IJCNLP","author":"Huang Shen","year":"2017","unstructured":"Shen Huang, Xu Sun, and Houfeng Wang. 2017. Addressing domain adaptation for chinese word segmentation with global recurrent structure. In Proceedings of the IJCNLP. 184\u2013193."},{"key":"e_1_3_2_24_2","unstructured":"Zhiheng Huang Wei Xu and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991 (2015)."},{"key":"e_1_3_2_25_2","first-page":"5001","volume-title":"Proceedings of the CVPR","author":"Inoue Naoto","year":"2018","unstructured":"Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa. 2018. Cross-domain weakly-supervised object detection through progressive domain adaptation. In Proceedings of the CVPR. 5001\u20135009."},{"key":"e_1_3_2_26_2","first-page":"2464","volume-title":"Proceedings of the ACL","author":"Jia Chen","year":"2019","unstructured":"Chen Jia, Xiaobo Liang, and Yue Zhang. 2019. Cross-domain NER using cross-domain language modeling. In Proceedings of the ACL. 2464\u20132474."},{"key":"e_1_3_2_27_2","volume-title":"Proceedings of the EMNLP","author":"Jiang Peijie","year":"2021","unstructured":"Peijie Jiang, Dingkun Long, Yueheng Sun, Meishan Zhang, Guangwei Xu, and Pengjun Xie. 2021. A fine-grained domain adaption model for joint word segmentation and pos tagging. 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In Proceedings of the 2nd Workshop on Representation Learning for NLP."},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1007\/978-3-030-32236-6_69","volume-title":"Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing","author":"Peng Xue","year":"2019","unstructured":"Xue Peng, Zhenghua Li, Min Zhang, Rui Wang, Yue Zhang, and Luo Si. 2019. Overview of the nlpcc 2019 shared task: Cross-domain dependency parsing. In Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, 760\u2013771."},{"key":"e_1_3_2_51_2","first-page":"16282","article-title":"Universal domain adaptation through self supervision","volume":"33","author":"Saito Kuniaki","year":"2020","unstructured":"Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko. 2020. Universal domain adaptation through self supervision. 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In Proceedings of the IJCAI. 1707\u20131712."},{"key":"e_1_3_2_55_2","article-title":"A dirt-t approach to unsupervised domain adaptation","author":"Shu Rui","year":"2018","unstructured":"Rui Shu, Hung H. Bui, Hirokazu Narui, and Stefano Ermon. 2018. A dirt-t approach to unsupervised domain adaptation. In Proceedings of the ICLR.","journal-title":"Proceedings of the ICLR"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.2200\/S00497ED1V01Y201304HLT021"},{"key":"e_1_3_2_57_2","unstructured":"Kihyuk Sohn David Berthelot Nicholas Carlini Zizhao Zhang Han Zhang Colin A. Raffel Ekin Dogus Cubuk Alexey Kurakin and Chun-Liang Li. 2020. FixMatch: Simplifying semi-supervised learning with consistency and confidence. Advances in Neural Information Processing Systems 33 (2020) 596\u2013608."},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2014.12.003"},{"key":"e_1_3_2_59_2","first-page":"1385","volume-title":"Proceedings of the ACL","author":"Sun Weiwei","year":"2011","unstructured":"Weiwei Sun. 2011. A stacked sub-word model for joint chinese word segmentation and part-of-speech tagging. In Proceedings of the ACL. 1385\u20131394."},{"key":"e_1_3_2_60_2","first-page":"8286","volume-title":"Proceedings of the ACL","author":"Tian Yuanhe","year":"2020","unstructured":"Yuanhe Tian, Yan Song, Xiang Ao, Fei Xia, Xiaojun Quan, Tong Zhang, and Yonggang Wang. 2020. Joint chinese word segmentation and part-of-speech tagging via two-way attentions of auto-analyzed knowledge. 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In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 8274\u20138285."},{"key":"e_1_3_2_63_2","volume-title":"Proceedings of the HLT-NAACL 2003","author":"Toutanova Kristina","year":"2003","unstructured":"Kristina Toutanova, Dan Klein, Christopher Manning, and Yoram Singer. 2003. Feature-rich part-of-speech tagging with a cyclic dependency network.. In Proceedings of the HLT-NAACL 2003."},{"key":"e_1_3_2_64_2","first-page":"7167","volume-title":"Proceedings of the CVPR","author":"Tzeng Eric","year":"2017","unstructured":"Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. 2017. Adversarial discriminative domain adaptation. In Proceedings of the CVPR. 7167\u20137176."},{"key":"e_1_3_2_65_2","first-page":"2302","volume-title":"Proceedings of the EMNLP","author":"\u00dcst\u00fcn Ahmet","year":"2020","unstructured":"Ahmet \u00dcst\u00fcn, Arianna Bisazza, Gosse Bouma, and Gertjan van Noord. 2020. 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