{"id":168185,"date":"2013-08-01T00:00:00","date_gmt":"2013-08-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/bilingual-data-cleaning-for-smt-using-graph-based-random-walk\/"},"modified":"2020-12-27T19:16:51","modified_gmt":"2020-12-28T03:16:51","slug":"bilingual-data-cleaning-for-smt-using-graph-based-random-walk","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/bilingual-data-cleaning-for-smt-using-graph-based-random-walk\/","title":{"rendered":"Bilingual Data Cleaning for SMT using Graph-based Random Walk"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The quality of bilingual data is a key factor<br \/>\nin Statistical Machine Translation (SMT).<br \/>\nLow-quality bilingual data tends to produce<br \/>\nincorrect translation knowledge and<br \/>\nalso degrades translation modeling performance.<br \/>\nPrevious work often used supervised<br \/>\nlearning methods to filter lowquality<br \/>\ndata, but a fair amount of human<br \/>\nlabeled examples are needed which are<br \/>\nnot easy to obtain. To reduce the reliance<br \/>\non labeled examples, we propose<br \/>\nan unsupervised method to clean bilingual<br \/>\ndata. The method leverages the mutual<br \/>\nreinforcement between the sentence<br \/>\npairs and the extracted phrase pairs, based<br \/>\non the observation that better sentence<br \/>\npairs often lead to better phrase extraction<br \/>\nand vice versa. End-to-end experiments<br \/>\nshow that the proposed method substantially<br \/>\nimproves the performance in largescale<br \/>\nChinese-to-English translation tasks.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The quality of bilingual data is a key factor in Statistical Machine Translation (SMT). Low-quality bilingual data tends to produce incorrect translation knowledge and also degrades translation modeling performance. Previous work often used supervised learning methods to filter lowquality data, but a fair amount of human labeled examples are needed which are not easy to [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Lei Cui","user_id":"32631"},{"type":"user_nicename","value":"Dongdong Zhang","user_id":"31677"},{"type":"user_nicename","value":"Shujie Liu","user_id":"33634"},{"type":"user_nicename","value":"Shujie Liu","user_id":"33633"},{"type":"user_nicename","value":"Mu Li","user_id":"33033"},{"type":"user_nicename","value":"Ming Zhou","user_id":"32942"}],"msr_publishername":"ACL - Association for Computational 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