{"id":165496,"date":"2013-09-01T00:00:00","date_gmt":"2013-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/learning-semantic-representations-for-the-phrase-translation-model\/"},"modified":"2018-10-16T20:46:55","modified_gmt":"2018-10-17T03:46:55","slug":"learning-semantic-representations-for-the-phrase-translation-model","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-semantic-representations-for-the-phrase-translation-model\/","title":{"rendered":"Learning Semantic Representations for the Phrase Translation Model"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper presents a novel semantic-based phrase translation model. A pair of source and target phrases are projected into continuous-valued vector representations in a low-dimensional latent semantic space, where their translation score is computed by the distance between the pair in this new space. The projection is performed by a multi-layer neural net-work whose weights are learned on parallel training data. The learning is aimed to directly optimize the quality of end-to-end machine translation results. Experimental evaluation has been performed on two Europarl translation tasks, English-French and German-English. The results show that the new semantic-based phrase translation model significantly improves the performance of a state-of-the-art phrase-based statistical machine translation system, leading to a gain of 0.7-1.0 BLEU points.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a novel semantic-based phrase translation model. A pair of source and target phrases are projected into continuous-valued vector representations in a low-dimensional latent semantic space, where their translation score is computed by the distance between the pair in this new space. The projection is performed by a multi-layer neural net-work whose weights 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