Combining multi-domain statistical machine translation models using automatic classifiers
Banerjee, Pratyush, Du, JinhuaORCID: 0000-0002-3267-4881, Li, Baoli, Kumar Naskar, Sudip, Way, AndyORCID: 0000-0001-5736-5930 and van Genabith, JosefORCID: 0000-0003-1322-7944
(2010)
Combining multi-domain statistical machine translation models using automatic classifiers.
In: AMTA 2010 - 9th Conference of the Association for Machine Translation in the Americas, 31 October - 4 November 2010, Denver, CO, USA.
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.