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

We build parallel FDA5 (ParFDA) Moses statistical machine translation (SMT) systems for all language pairs in the workshop on statistical machine translation (Bojar et al., 2015) (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 (Biçici, 2013; Biçici et al., 2014; Biçici and Yuret, 2015). 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. 1 Parallel FDA5 (ParFDA) Statistical machine translation performance is influenced by the data: if you already have the translations for the source being translated in your training set or even portions of it, then the translation task becomes easier. If some token does not appear in your language model (LM), then it becomes harder for the SMT engine to find its correct position in the translation. The importance of ParFDA increases with the proliferation of training material available for building SMT systems. Table 1 presents the statistics of the available training and LM corpora for the constrained (C) systems in WMT15 (Bojar et al., 2015) as well as the statistics of the ParFDA selected training and LM data. ParFDA (Biçici, 2013; Biçici et al., 2014) runs separate FDA5 (Biçici and Yuret, 2015) models on randomized subsets of the training data and combines the selections afterwards. FDA5 is available at http://github.com/bicici/FDA. We run ParFDA SMT experiments using Moses (Koehn et al., 2007) in all language pairs in WMT15 (Bojar et al., 2015) and obtain SMT performance close to the top constrained Moses systems. ParFDA allows rapid prototyping of SMT systems for a given target domain or task. We use ParFDA for selecting parallel training data and LM data for building SMT systems. We select the LM training data with ParFDA based on the following observation (Biçici, 2013): No word not appearing in the training set can appear in the translation. Thus we are only interested in correctly ordering the words appearing in the training corpus and collecting the sentences that contain them for building the LM. At the same time, a compact and more relevant LM corpus is also useful for modeling longer range dependencies with higher order ngram models. We use 3-grams for selecting training data and 2-grams for LM corpus selection.