Phrase-based SMT & MERT (2003) › Implementation and Toolkits
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Phrase-based statistical machine translation with MERT became the dominant approach through the 2000s, supported by reliable open-source toolkits. The Moses toolkit, developed by researchers including Philipp Koehn, became the standard implementation. This provided tools for training phrase-based systems, extracting phrase tables, tuning with MERT, and decoding translations. Moses made phrase-based translation accessible to researchers and practitioners. This enabled widespread adoption and further research.
The Moses toolkit workflow encapsulated the phrase-based approach. Training began with word alignment using tools like GIZA++, which implemented the IBM models to produce word-level alignments. From these alignments, Moses extracted phrase pairs meeting consistency constraints, building phrase tables with translation probabilities. Language models were trained separately on target language corpora. During decoding, Moses used dynamic programming and beam search to find high-probability translations, combining phrase translation probabilities, language model scores, reordering costs, and other features in a log-linear model.
MERT tuning was integrated into the Moses workflow. Given trained phrase tables and language models, MERT optimized feature weights on development data. The tuning process required generating translations multiple times as weights were adjusted, making it computationally intensive but essential for good performance. Moses provided MERT as the default tuning method, with alternatives available for systems requiring more features or greater stability.
Moses and similar toolkits demonstrated the practical viability of phrase-based translation. Systems built with these tools achieved production-quality translations for many language pairs, powering commercial translation services and research applications. The open-source availability of Moses enabled rapid progress, as researchers could build on existing implementations rather than developing systems from scratch. This accessibility accelerated innovation and established phrase-based translation as the standard statistical approach.