{"type":"ScholarlyArticle","title":"Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification","authors":[{"name":"Menglong Lu","slug":"menglong-lu","given":"Menglong","family":"Lu","affiliation":"National Key Laboratory of Parallel and Distributed Computing , National University of Defense Technology , China"},{"name":"Zhen Huang","slug":"zhen-huang","given":"Zhen","family":"Huang","affiliation":"National Key Laboratory of Parallel and Distributed Computing , National University of Defense Technology , China"},{"name":"Zhiliang Tian","slug":"zhiliang-tian","given":"Zhiliang","family":"Tian","affiliation":"National Key Laboratory of Parallel and Distributed Computing , National University of Defense Technology , China"},{"name":"Yunxiang Zhao","slug":"yunxiang-zhao","given":"Yunxiang","family":"Zhao","affiliation":"Beijing Institute of Biotechnology , China"},{"name":"Xuanyu Fei","slug":"xuanyu-fei","given":"Xuanyu","family":"Fei","affiliation":"Suiren Information , China"},{"name":"Dongsheng Li","slug":"dongsheng-li","given":"Dongsheng","family":"Li","affiliation":"National Key Laboratory of Parallel and Distributed Computing , National University of Defense Technology , China"}],"abstract":"Text classification is a fundamental task for natural language processing, and adapting text classification models across domains has broad applications. \r\nSelf-training generates pseudo-examples from the model's predictions and iteratively trains on the pseudo-examples, i.e., minimizes the loss on the source domain and the Gibbs entropy on the target domain. However, Gibbs entropy is sensitive to prediction errors, and thus, self-training tends to fail when the domain shift is large. In this paper, we propose Meta-Tsallis Entropy minimization (MTEM). MTEM uses an instance adaptive Tsallis entropy to replace the Gibbs entropy and a meta-learning algorithm to optimize the instance adaptive Tsallis entropy on the target domain. To reduce the computation cost of MTEM, we propose an approximation technique to approximate the second-order derivation involved in the meta-learning. To efficiently generate pseudo labels, we propose an annealing sampling mechanism for exploring the model's prediction probability. Theoretically, we prove the convergence of the meta-learning algorithm in MTEM and analyze the effectiveness of MTEM in achieving domain adaptation. Experimentally, MTEM improves the adaptation performance of BERT with an average of 4 percent on the benchmark dataset.","doi":"10.24963/ijcai.2023/573","url":"https://ijcai.org/proceedings/2023/573","pdf":"https://ijcai.org/proceedings/2023/0573.pdf","markdown":"https://ijcai.org/proceedings/2023/573.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/573","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Natural Language Processing","track":"Main Track","number":573,"pages":{"first":5159,"last":5169},"keywords":["Natural Language Processing: NLP: Applications","Natural Language Processing: NLP: Text classification"],"videos":[],"references":[{"position":1,"raw":"[Benson, 1995] Harold P Benson. 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