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Process."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>Multi-domain neural machine translation aims to construct a unified neural machine translation model to translate sentences across various domains. Nevertheless, previous studies have one limitation is the incapacity to acquire both domain-general and domain-specific representations concurrently. To this end, we propose an ensemble strategy with gradient conflict for multi-domain neural machine translation that automatically learns model parameters by identifying both domain-shared and domain-specific features. Specifically, our approach consists of (1) a parameter-sharing framework, where the parameters of all the layers are originally shared and equivalent to each domain, and (2) ensemble strategy, in which we design an Extra Ensemble strategy via a piecewise condition function to learn direction and distance-based gradient conflict. In addition, we give a detailed theoretical analysis of the gradient conflict to further validate the effectiveness of our approach. Experimental results on two multi-domain datasets show the superior performance of our proposed model compared to previous work.<\/jats:p>","DOI":"10.1145\/3638248","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T11:54:08Z","timestamp":1703159648000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["An Ensemble Strategy with Gradient Conflict for Multi-Domain Neural Machine Translation"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3336-5703","authenticated-orcid":false,"given":"Zhibo","family":"Man","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7308-0146","authenticated-orcid":false,"given":"Yujie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0569-2267","authenticated-orcid":false,"given":"Yu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2572-9300","authenticated-orcid":false,"given":"Yuanmeng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0437-6788","authenticated-orcid":false,"given":"Yufeng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0170-626X","authenticated-orcid":false,"given":"Jinan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China"}]}],"member":"320","published-online":{"date-parts":[[2024,2,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.692"},{"key":"e_1_3_2_3_2","volume-title":"Proceedings of the 3rd International Conference on Learning Representations (ICLR \u201915)","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. 2015. 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