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The NAIST Machine Translation System for IWSLT2012
BibTeX
@MISC{Neubig_thenaist,
author = {Graham Neubig and Kevin Duh and Masaya Ogushi and Takamoto Kano and Tetsuo Kiso and Sakriani Sakti and Tomoki Toda and Satoshi Nakamura},
title = {The NAIST Machine Translation System for IWSLT2012},
year = {}
}
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Abstract
This paper describes the NAIST statistical machine translation system for the IWSLT2012 Evaluation Campaign. We participated in all TED Talk tasks, for a total of 11 languagepairs. For all tasks, we use the Moses phrase-based decoder and its experiment management system as a common base for building translation systems. The focus of our work is on performing a comprehensive comparison of a multitude of existing techniques for the TED task, exploring issues such as out-of-domain data filtering, minimum Bayes risk decoding, MERT vs. PRO tuning, word alignment combination, and morphology.
Keyphrases
naist machine translation system experiment management system naist statistical machine translation system out-of-domain data filtering word alignment combination iwslt2012 evaluation campaign ted task comprehensive comparison pro tuning mert v common base translation system minimum bayes risk decoding moses phrase-based decoder ted talk task
