PJIIT’s systems for WMT 2017 Conference

Abstract

In this paper, we attempt to improve Statistical Machine Translation (SMT) systems between Czech, Latvian and English in WNT'17 News translation task. We also participated in the Biomedical task and produces translation engines from English into Polish, Czech, German, Spanish, French, Hungarian, Romanian and Swedish. To accomplish this, we performed translation model training, created adaptations of training settings for each language pair, and implemented BPE (subword units) for our SMT systems. Innovative tools and data adaptation techniques were employed. Only the official parallel text corpora and monolingual models for the WMT 2017 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.

Findings of the WMT 2017 Biomedical Translation Shared Task

“…PJIIT developed a translation model training, created adaptations of training settings for each language pair, and implemented byte pair encoding (BPE) (subword units) in their systems (Wolk and Marasek, 2017). Only the official parallel text corpora and monolingual models for the challenge evaluation campaign were used to train language models, and to develop, tune, and test their system.…”

“…The learning rate was set to 0.00001, initialized with a pre-trained model, and optimized using only the in-domain medical data. The HimL tun- (Wolk and Marasek, 2017). Only the official parallel text corpora and monolingual models for the challenge evaluation campaign were used to train language models, and to develop, tune, and test their system.…”