emnlp emnlp2011 emnlp2011-44 emnlp2011-44-reference knowledge-graph by maker-knowledge-mining

44 emnlp-2011-Domain Adaptation via Pseudo In-Domain Data Selection


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Author: Amittai Axelrod ; Xiaodong He ; Jianfeng Gao

Abstract: Xiaodong He Microsoft Research Redmond, WA 98052 xiaohe @mi cro s o ft . com Jianfeng Gao Microsoft Research Redmond, WA 98052 j fgao @mi cro s o ft . com have its own argot, vocabulary or stylistic preferences, such that the corpus characteristics will necWe explore efficient domain adaptation for the task of statistical machine translation based on extracting sentences from a large generaldomain parallel corpus that are most relevant to the target domain. These sentences may be selected with simple cross-entropy based methods, of which we present three. As these sentences are not themselves identical to the in-domain data, we call them pseudo in-domain subcorpora. These subcorpora 1% the size of the original can then used to train small domain-adapted Statistical Machine Translation (SMT) systems which outperform systems trained on the entire corpus. Performance is further improved when we use these domain-adapted models in combination with a true in-domain model. The results show that more training data is not always better, and that best results are attained via proper domain-relevant data selection, as well as combining in- and general-domain systems during decoding. – –


reference text

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