Edinburgh's Machine Translation Systems for European Language Pairs
Nadir Durrani, Barry Haddow, Kenneth Heafield, Philipp Koehn · 2013
We validated various novel and recently proposed methods for statistical machine translation on 10 language pairs, using large data resources. We saw gains from optimizing parameters, training with sparse features, the operation sequence model, and domain adaptation techniques. We also report on utilizing a huge language model trained on 126 billion tokens. The annual machine translation evaluation campaign for European languages organized around the ACL Workshop on Statistical Machine Translation offers the opportunity to test recent advancements in machine translation in large data condition across several diverse language pairs. Building on our own developments and external contributions to the Moses open source toolkit, we carried out extensive experiments that, by early indications, led to a strong showing in the evaluation campaign. We would like to stress especially two contributions: the use of the new operation sequence model (Section 3) within Moses, and — in a separate unconstraint track submission — the use of a huge language model trained on 126 billion tokens with a new training tool (Section 4).