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    <abstract>Most attempts at integrating word sense disambiguation with statistical machine translation have focused on supervised disambiguation approaches. These approaches are of limited use when the distribution of the test data differs strongly from that of the training data; however, word sense errors tend to be especially common under these conditions. In this paper we present different approaches to unsupervised word translation disambiguation and apply them to the problem of translating conversational speech under resource-poor training conditions. Both human and automatic evaluation metrics demonstrate significant improvements resulting from our technique.</abstract>
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%0 Conference Proceedings
%T Unsupervised Translation Disambiguation for Cross-Domain Statistical Machine Translation
%A Yang, Mei
%A Kirchhoff, Katrin
%S Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2012
%8 oct 28 nov 1
%I Association for Machine Translation in the Americas
%C San Diego, California, USA
%F yang-kirchhoff-2012-unsupervised
%X Most attempts at integrating word sense disambiguation with statistical machine translation have focused on supervised disambiguation approaches. These approaches are of limited use when the distribution of the test data differs strongly from that of the training data; however, word sense errors tend to be especially common under these conditions. In this paper we present different approaches to unsupervised word translation disambiguation and apply them to the problem of translating conversational speech under resource-poor training conditions. Both human and automatic evaluation metrics demonstrate significant improvements resulting from our technique.
%U https://aclanthology.org/2012.amta-papers.29/

Markdown (Informal)

[Unsupervised Translation Disambiguation for Cross-Domain Statistical Machine Translation](https://aclanthology.org/2012.amta-papers.29/) (Yang & Kirchhoff, AMTA 2012)

• Unsupervised Translation Disambiguation for Cross-Domain Statistical Machine Translation (Yang & Kirchhoff, AMTA 2012)

ACL

• Mei Yang and Katrin Kirchhoff. 2012. Unsupervised Translation Disambiguation for Cross-Domain Statistical Machine Translation. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers, San Diego, California, USA. Association for Machine Translation in the Americas.