: The performance of a statistical machine translation (SMT) system heavily depends on the quantity and quality of the bilingual language resource. However, the pervious work mainl...
Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in th...
We propose a domain specific model for statistical machine translation. It is wellknown that domain specific language models perform well in automatic speech recognition. We show ...
Automatic speech recognition (ASR) results contain not only ASR errors, but also disfluencies and colloquial expressions that must be corrected to create readable transcripts. We...
Graham Neubig, Yuya Akita, Shinsuke Mori, Tatsuya ...
Decoding algorithm is a crucial part in statistical machine translation. We describe a stack decoding algorithm in this paper. We present the hypothesis scoring method and the heu...
In this paper, we describe a search procedure for statistical machine translation (MT) based on dynmnic programming (DP). Starting from a DP-based solution to the traveling salesm...
In this paper, we t)resent and compare various alignnmnt models for statistical machine translation. We propose to measure tile quality of an aligmnent model using the quality of ...
In the framework of statistical machine translation (SMT), correspondences between the words in the source and the target language are learned from bilingual corpora on the basis ...
Most statistical machine translation systems employ a word-based alignment model. In this paper we demonstrate that word-based alignment is a major cause of translation errors. We...
We present improvements to a greedy decoding algorithm for statistical machine translation that reduce its time complexity from at least cubic ( ¢¡¤£¦¥¨§ when applied na...