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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,3,31]]},"abstract":"<jats:p>\n            Considering the expensive annotation in\n            <jats:bold>Named Entity Recognition (NER<\/jats:bold>\n            ), Cross-domain NER enables NER in low-resource target domains with few or without labeled data, by transferring the knowledge of high-resource domains. However, the discrepancy between different domains causes the domain shift problem and hampers the performance of cross-domain NER in low-resource scenarios. In this article, we first propose an adversarial adaptive augmentation, where we integrate the adversarial strategy into a multi-task learner to augment and qualify domain adaptive data. We extract domain-invariant features of the adaptive data to bridge the cross-domain gap and alleviate the label-sparsity problem simultaneously. Therefore, another important component in this article is the progressive domain-invariant feature distillation framework. A multi-grained\n            <jats:bold>MMD (Maximum Mean Discrepancy)<\/jats:bold>\n            approach in the framework to extract the multi-level domain invariant features and enable knowledge transfer across domains through the adversarial adaptive data. Advanced\n            <jats:bold>Knowledge Distillation (KD)<\/jats:bold>\n            schema processes progressively domain adaptation through the powerful pre-trained language models and multi-level domain invariant features. Extensive comparative experiments over four English and two Chinese benchmarks show the importance of adversarial augmentation and effective adaptation from high-resource domains to low-resource target domains. Comparison with two vanilla and four latest baselines indicates the state-of-the-art performance and superiority confronted with both zero-resource and minimal-resource scenarios.\n          <\/jats:p>","DOI":"10.1145\/3570502","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T12:14:04Z","timestamp":1671020044000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Domain-Invariant Feature Progressive Distillation with Adversarial Adaptive Augmentation for Low-Resource Cross-Domain NER"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8882-3148","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7581-0882","authenticated-orcid":false,"given":"Congying","family":"Xia","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1525-1067","authenticated-orcid":false,"given":"Zhiwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Salesforce AI Research, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7647-3603","authenticated-orcid":false,"given":"Shu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0458-5977","authenticated-orcid":false,"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-4419"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.5555\/1699765.1699767"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"e_1_3_2_5_2","first-page":"261","volume-title":"2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019","author":"Cao Yixin","year":"2020","unstructured":"Yixin Cao, Zikun Hu, Tat Seng Chua, Zhiyuan Liu, and Heng Ji. 2020. 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