Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/136499
Title: NEW ADVANCES IN REORDERING FOR STATISTICAL MACHINE TRANSLATION
Authors: CHRISTIAN HADIWINOTO
Keywords: reordering, machine translation, natural language processing, computer science
Issue Date: 11-May-2017
Source: CHRISTIAN HADIWINOTO (2017-05-11). NEW ADVANCES IN REORDERING FOR STATISTICAL MACHINE TRANSLATION. ScholarBank@NUS Repository.
Abstract: Phrase-based statistical machine translation delivers good performance for machine translation. Nevertheless, the difference in word order between different languages poses a major challenge to this approach, especially for language pairs with significant differences in word order. This thesis tackles the reordering problem by exploiting dependency parse trees in the phrase-based statistical machine translation approach. We propose a novel approach to detect translation ordering of two words and apply sparse dependency swap features in translation decoding to encourage good translation output word order, which gives a significant improvement in Chinese-to-English translation. We then design a neural dependency-based reordering model applied within phrase-based translation decoding, resulting in a further improvement on Chinese-to-English translation. Experiments on other language pairs further demonstrate the strength of our proposed approach. We also explore system combination with the recently proposed end-to-end neural machine translation, which shows the competitiveness of our proposed approach.
URI: http://scholarbank.nus.edu.sg/handle/10635/136499
Appears in Collections:Ph.D Theses (Open)

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