ParFDA for Fast Deployment of Accurate Statistical Machine Translation Systems, Benchmarks, and Statistics

Ergun Biçici, Qun Liu, and Andy Way. ParFDA for Fast Deployment of Accurate Statistical Machine Translation Systems, Benchmarks, and Statistics. InProceedings of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation, Lisbon, Portugal, September 2015. Association for Computational Linguistics. [WWW] Keyword(s): Machine Translation, Machine Learning, Language Modeling.

We build parallel FDA5 (ParFDA) Moses statistical machine translation (SMT) systems for all language pairs in the workshop on statistical machine translation~\cite{WMT2015} (WMT15) translation task and obtain results close to the top with an average of $3.176$ BLEU points difference using significantly less resources for building SMT systems. ParFDA is a parallel implementation of feature decay algorithms (FDA) developed for fast deployment of accurate SMT systems. ParFDA Moses SMT system we built is able to obtain the top TER performance in French to English translation. We make the data for building ParFDA Moses SMT systems for WMT15 available: https://github.com/bicici/ParFDAWMT15.