{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:57:11Z","timestamp":1757545031017},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Most translation tasks among languages belong to the zero-resource translation problem where parallel corpora are unavailable. Multilingual neural machine translation (MNMT) enables one-pass translation using shared semantic space for all languages compared to the two-pass pivot translation but often underperforms the pivot-based method. In this paper, we propose a novel method, named as Unified Multilingual Multiple teacher-student Model for NMT (UM4). Our method unifies source-teacher, target-teacher, and pivot-teacher models to guide the student model for the zero-resource translation. The source teacher and target teacher force the student to learn the direct source-target translation by the distilled knowledge on both source and target sides. The monolingual corpus is further leveraged by the pivot-teacher model to enhance the student model. Experimental results demonstrate that our model of 72 directions significantly outperforms previous methods on the WMT benchmark.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/618","type":"proceedings-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T22:55:56Z","timestamp":1657925756000},"page":"4454-4460","source":"Crossref","is-referenced-by-count":7,"title":["UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation"],"prefix":"10.24963","author":[{"given":"Jian","family":"Yang","sequence":"first","affiliation":[{"name":"Beihang University"}]},{"given":"Yuwei","family":"Yin","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia"}]},{"given":"Shuming","family":"Ma","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia"}]},{"given":"Dongdong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia"}]},{"given":"Shuangzhi","family":"Wu","sequence":"additional","affiliation":[{"name":"Tencent Cloud Xiaowei"}]},{"given":"Hongcheng","family":"Guo","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia"}]},{"given":"Zhoujun","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University"}]},{"given":"Furu","family":"Wei","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T07:10:43Z","timestamp":1658128243000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/618"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/618","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}