{"action":"opened","issue":{"url":"https://api.github.com/repos/OnizukaLab/ConferenceProceedings/issues/25","repository_url":"https://api.github.com/repos/OnizukaLab/ConferenceProceedings","labels_url":"https://api.github.com/repos/OnizukaLab/ConferenceProceedings/issues/25/labels{/name}","comments_url":"https://api.github.com/repos/OnizukaLab/ConferenceProceedings/issues/25/comments","events_url":"https://api.github.com/repos/OnizukaLab/ConferenceProceedings/issues/25/events","html_url":"https://github.com/OnizukaLab/ConferenceProceedings/issues/25","id":361098865,"node_id":"MDU6SXNzdWUzNjEwOTg4NjU=","number":25,"title":"Triangular Architecture for Rare Language Translation","user":{"login":"knccch","id":13745724,"node_id":"MDQ6VXNlcjEzNzQ1NzI0","avatar_url":"https://avatars2.githubusercontent.com/u/13745724?v=4","gravatar_id":"","url":"https://api.github.com/users/knccch","html_url":"https://github.com/knccch","followers_url":"https://api.github.com/users/knccch/followers","following_url":"https://api.github.com/users/knccch/following{/other_user}","gists_url":"https://api.github.com/users/knccch/gists{/gist_id}","starred_url":"https://api.github.com/users/knccch/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/knccch/subscriptions","organizations_url":"https://api.github.com/users/knccch/orgs","repos_url":"https://api.github.com/users/knccch/repos","events_url":"https://api.github.com/users/knccch/events{/privacy}","received_events_url":"https://api.github.com/users/knccch/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-09-18T01:11:18Z","updated_at":"2018-09-18T01:11:18Z","closed_at":null,"author_association":"NONE","body":"一言でいうと\r\n------------------------------------\r\nresource-richの言語対(X, Y)を用いてresource-poorの言語対(X, Z), (Y, Z)の翻訳精度を上げるのに、triangular architectureを提案。\r\n\r\n### 論文リンク\r\nhttp://aclweb.org/anthology/P18-1006\r\n\r\n### 著者/所属機関\r\nShuo Ren1,2*, Wenhu Chen3, Shujie Liu4, Mu Li4, Ming Zhou4 and Shuai Ma1,2\r\n1SKLSDE Lab, Beihang University, China\r\n2Beijing Advanced Innovation Center for Big Data and Brain Computing     \r\n3University of California, Santa Barbara, CA, USA\r\n4Microsoft Research Asia, Beijing, China\r\n\r\n### 投稿日付(yyyy/MM/dd)\r\n2018/05/13\r\n\r\n概要\r\n------------------------------------\r\nNeural Machine Translation (NMT) performs poor on the low-resource language pair (X, Z), especially when Z is a rare language. By introducing another rich language Y , we propose a novel triangular training architecture (TA-NMT) to leverage bilingual data (Y, Z) (may be small) and (X, Y ) (can be rich) to improve the translation performance of low-resource pairs. In this triangular architecture, Z is taken as the intermediate latent variable, and translation models of Z are jointly optimized with a unified bidirectional EM algorithm under the goal of maximizing the translation likelihood of (X, Y ). Empirical results demonstrate that our method significantly improves the translation quality of rare languages on MultiUN and IWSLT2012 datasets, and achieves even better performance combining back-translation methods.\r\n\r\n新規性・差分\r\n------------------------------------\r\n* triangular architectureを提案\r\n* bidirectional EMによる訓練\r\n\r\n手法\r\n------------------------------------\r\n![ta_nmt](https://user-images.githubusercontent.com/13745724/45658434-d536ed00-bb29-11e8-8217-de009a08c8fc.png)\r\n\r\n結果\r\n------------------------------------\r\n![results](https://user-images.githubusercontent.com/13745724/45658472-fdbee700-bb29-11e8-877c-9b8e01e35c2d.png)\r\n\r\nコメント\r\n------------------------------------\r\nMultilingual MTと実験的に比べてない"}}
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