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Improving Neural Net Machine Translation Systems with Linguistic Information

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자료유형E-Book
개인저자Chen, Yuan-Lu.
단체저자명The University of Arizona. Linguistics.
서명/저자사항Improving Neural Net Machine Translation Systems with Linguistic Information.
발행사항[S.l.] : The University of Arizona., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항91 p.
소장본 주기School code: 0009.
ISBN9780438301184
일반주기 Source: Dissertation Abstracts International, Volume: 80-01(E), Section: A.
Adviser: Michael Hammond.
요약Interlinear Glossed Text (IGT) is widely used in linguistic studies. In a form of Interlinear Glossed Text, the first line is a sentence of the language of interest, the second line is a word-by-word translation, annotated with relevant grammati
요약The innovation of the current work is to incorporate the gloss information of Interlinear Glossed Text data into neural net machine translation systems.
요약Critically, if the Gaelic data and the gloss data are combined in a specific way as the training data, which is named as Parallel-Partial treatment, the performance of the systems is improved significantly. The systems with Parallel-Partial trea
요약Moreover, the boosting effect of the Parallel-Partial treatment is consistent across different languages and across neural net machine translation systems with different hyper-parameter settings.
요약How theoretical linguistics may work hand in hand with natural language processing, and how neural net machine learning may exploit linguistics are important questions (Pater 2017). The current work also exemplifies how theoretical linguistics m
일반주제명Linguistics.
언어영어
기본자료 저록Dissertation Abstracts International80-01A(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T15000949

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