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        <title>Combining Multi-Domain Statistical Machine Translation Models using Automatic Classifiers</title>
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        <namePart type="given">Pratyush</namePart>
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    <abstract>This paper presents a set of experiments on Domain Adaptation of Statistical Machine Translation systems. The experiments focus on Chinese-English and two domain-specific corpora. The paper presents a novel approach for combining multiple domain-trained translation models to achieve improved translation quality for both domain-specific as well as combined sets of sentences. We train a statistical classifier to classify sentences according to the appropriate domain and utilize the corresponding domain-specific MT models to translate them. Experimental results show that the method achieves a statistically significant absolute improvement of 1.58 BLEU (2.86% relative improvement) score over a translation model trained on combined data, and considerable improvements over a model using multiple decoding paths of the Moses decoder, for the combined domain test set. Furthermore, even for domain-specific test sets, our approach works almost as well as dedicated domain-specific models and perfect classification.</abstract>
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%0 Conference Proceedings
%T Combining Multi-Domain Statistical Machine Translation Models using Automatic Classifiers
%A Banerjee, Pratyush
%A Du, Jinhua
%A Li, Baoli
%A Naskar, Sudip
%A Way, Andy
%A van Genabith, Josef
%S Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2010
%8 oct 31 nov 4
%I Association for Machine Translation in the Americas
%C Denver, Colorado, USA
%F banerjee-etal-2010-combining
%X This paper presents a set of experiments on Domain Adaptation of Statistical Machine Translation systems. The experiments focus on Chinese-English and two domain-specific corpora. The paper presents a novel approach for combining multiple domain-trained translation models to achieve improved translation quality for both domain-specific as well as combined sets of sentences. We train a statistical classifier to classify sentences according to the appropriate domain and utilize the corresponding domain-specific MT models to translate them. Experimental results show that the method achieves a statistically significant absolute improvement of 1.58 BLEU (2.86% relative improvement) score over a translation model trained on combined data, and considerable improvements over a model using multiple decoding paths of the Moses decoder, for the combined domain test set. Furthermore, even for domain-specific test sets, our approach works almost as well as dedicated domain-specific models and perfect classification.
%U https://aclanthology.org/2010.amta-papers.16

Markdown (Informal)

[Combining Multi-Domain Statistical Machine Translation Models using Automatic Classifiers](https://aclanthology.org/2010.amta-papers.16) (Banerjee et al., AMTA 2010)

• Combining Multi-Domain Statistical Machine Translation Models using Automatic Classifiers (Banerjee et al., AMTA 2010)

ACL

• Pratyush Banerjee, Jinhua Du, Baoli Li, Sudip Naskar, Andy Way, and Josef van Genabith. 2010. Combining Multi-Domain Statistical Machine Translation Models using Automatic Classifiers. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA. Association for Machine Translation in the Americas.