Improving fast\_align by Reordering
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Machine TranslationWord AlignmentSimilar Papers 제목 키워드 기반
Fast and Accurate Reordering with ITG Transition RNN
Attention-based sequence-to-sequence neural network models learn to jointly align and translate. The quadratic-time attention mechanism is powerful as it is capable of handling arbitrary long-distance reordering, but com…
DecoderFeature EngineeringMachine TranslationMorphological Inflection+3Word Alignment-Based Reordering of Source Chunks in PB-SMT
Reordering poses a big challenge in statistical machine translation between distant language pairs. The paper presents how reordering between distant language pairs can be handled efficiently in phrase-based statistical …
Machine TranslationTranslationWord AlignmentNeural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation
This paper presents an improved lexicalized reordering model for phrase-based statistical machine translation using a deep neural network. Lexicalized reordering suffers from reordering ambiguity, data sparseness and noi…
Machine TranslationTranslationWord AlignmentPhrase-based Machine Translation using Multiple Preordering Candidates
In this paper, we propose a new decoding method for phrase-based statistical machine translation which directly uses multiple preordering candidates as a graph structure. Compared with previous phrase-based decoding meth…
DecoderMachine TranslationTranslationAlignment-based reordering for SMT
We present a method for improving word alignment quality for phrase-based statistical machine translation by reordering the source text according to the target word order suggested by an initial word alignment. The reord…
Machine TranslationPart-Of-Speech TaggingTranslationWord Alignment