JU-USAAR: A Domain Adaptive MT System

By Koushik Pahari, Alapan Kuila, Santanu Pal, Sudip Kumar Naskar, Sivaji Bandyopadhyay, Josef van Genabith. 2016

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).

Subfields: Machine Translation

Citations in the corpus

• Findings of the 2016 Conference on Machine Translation (2016, neutral): “Pahari et al. (2016) describe JU-USAAR (Jadavpur University & Saarland University).”