Use of auxiliary translation for improving decoding in statistical machine translation

Abstract : Recently, the concept of driven decoding (DD), has been sucessfully applied to the automatic speech recognition (speech-to-text) task: an auxiliary transcription guide the decoding process. There is a strong interest in applying this concept to statistical machine translation (SMT). This paper presents our approach on this topic. Our first attempt in driven decoding consists in adding several feature functions corresponding to the distance between the current hypothesis decoded and the auxiliary translations available. Experimental results done for a french-to-english machine translation task, in the framework of the WMT 2011 evaluation, show the potential of the DD approach proposed.
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Benjamin Lecouteux, Laurent Besacier. Use of auxiliary translation for improving decoding in statistical machine translation. [Research Report] LIG. 2016. ⟨hal-01633286⟩

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