Abstract
This paper presents the JU-USAAR English-German domain adaptive machine translation (MT) system submitted to the IT domain translation task organized in WMT-2016 . Our system brings improvements over the in-domain baseline system by incorporating out-domain knowledge. We applied two methodologies to accelerate the performance of our in-domain MT system: (i) additional training material extraction from out-domain data using data selection method, and (ii) language model and translation model adaptation through interpolation. Our primary submission obtained a BLEU score of 34.5 (14.5 absolute and 72.5% relative improvements over baseline) and a TER score of 54.0 (14.7 absolute and 21.4% relative improvements over baseline).
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CITATION STYLE
Pahari, K., Kuila, A., Pal, S., Naskar, S. K., Bandyopadhyay, S., & Van Genabith, J. (2016). JU-USAAR: A Domain Adaptive MT System. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 2, pp. 442–448). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-2333
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