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Title: Reordering in statistical machine translation: A function word, syntax-based approach
Keywords: natural language processing, computational linguistics, statistical machine translation, phrase reordering, syntax-based approach, function words
Issue Date: 14-Aug-2009
Citation: HENDRA SETIAWAN (2009-08-14). Reordering in statistical machine translation: A function word, syntax-based approach. ScholarBank@NUS Repository.
Abstract: In this thesis, we investigate a specific area within Statistical Machine Translation (SMT): the reordering task -- the task of arranging translated words from source to target language order. This task is crucial as the failure to order words correctly leads to a disfluent discourse. This task is also challenging as it may require in-depth knowledge about the source and target language syntaxes, which are often not available to SMT models. We propose to address the reordering task by using knowledge of function words. In many languages, function words -- which include prepositions, determiners, articles, etc -- are important in explaining the grammatical relationship among phrases within a sentence. Projecting them and their dependent arguments into another language often results in structural changes in target sentence. Furthermore, function words have desirable empirical properties as they are enumerable and appear frequently in the text, making them highly amenable to statistical modeling.
Appears in Collections:Ph.D Theses (Open)

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