Combining Multi-Domain Statistical Machine Translation Models using Automatic Classifiers

Pratyush Banerjee, Jinhua Du, Baoli Li, Sudip Naskar, Andy Way, Josef van Genabith


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.
Anthology ID:
2010.amta-papers.16
Volume:
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 31-November 4
Year:
2010
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Denver, Colorado, USA
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AMTA
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Publisher:
Association for Machine Translation in the Americas
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URL:
https://aclanthology.org/2010.amta-papers.16
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PDF:
https://aclanthology.org/2010.amta-papers.16.pdf