Word Sense Disambiguation for Statistical Machine Translation
نویسنده
چکیده
While much effort has been put in designing and evaluating Word Sense Disambiguation (WSD) models for translation in the WSD community, standard Statistical Machine Translation (SMT) systems have achieved remarkable improvements in translation quality without modeling WSD explicitly. However, inspecting SMT output suggests that SMT needs better semantic modeling to accurately translate meaning. In the past few years, several approaches to directly tackle WSD in SMT have finally been proposed, and suggest that WSD has indeed something to offer to full-scale SMT. We will summarize our own efforts in integrating WSD models in SMT, and compare and contrast them with other recent work in WSD and context-dependent modeling for SMT.
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