Discourse in Statistical Machine Translation
نویسندگان
چکیده
منابع مشابه
Discourse in Statistical Machine Translation
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In the past decade, statistical machine translation (SMT) has been advanced from word-based SMT to phraseand syntax-based SMT. Although this advancement produces significant improvements in BLEU scores, crucial meaning errors and lack of cross-sentence connections at discourse level still hurt the quality of SMT-generated translations. More recently, we have witnessed two active movements in SM...
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The talk will show how the disambiguation of discourse connectives can improve their automatic translation. Connectives are a class of frequent functional lexical items that play an important role in text readability and coherence. Longer-range context is taken into account to learn the signaled rhetorical relations. The labels obtained from a discourse connective classifier are then integrated...
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This article shows how the automatic disambiguation of discourse connectives can improve Statistical Machine Translation (SMT) from English to French. Connectives are firstly disambiguated in terms of the discourse relation they signal between segments. Several classifiers trained using syntactic and semantic features reach stateof-the-art performance, with F1 scores of 0.6 to 0.8 over thirteen...
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Statistical Machine Translation is a modern success: Given a source language sentence, SMT finds the most probable target language sentence, based on (1) properties of the source; (2) probabilistic source--target mappings at the level of words, phrases and/or sub-structures; and (3) properties of the target language. SMT translates individual sentences because the search space even for a single...
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ژورنال
عنوان ژورنال: Discours
سال: 2012
ISSN: 1963-1723
DOI: 10.4000/discours.8726