PJAIT Systems for the WMT 2016

By K. Wołk, Krzysztof Marasek. 2016

In this paper, we attempt to improve Statistical Machine Translation (SMT) systems between Czech and English. To accomplish this, we performed translation model training, created adaptations of training settings for each language pair, and obtained comparable corpora for our SMT systems. Innovative tools and data adaptation techniques were employed. Only the official parallel text corpora and monolingual models for the WMT 2016 evaluation campaign were used to train language models, and to develop, tune, and test the system. We explored the use of domain adaptation techniques, symmetrized word alignment models, the unsupervised transliteration models and the KenLM language modeling tool. To evaluate the effects of different preparations on translation results, we conducted experiments and used the BLEU, NIST and TER metrics. Our results indicate that our approach produced a positive impact on SMT quality.

Subfields: Machine Translation