Explicit length modelling for statistical machine translation

Explicit length modelling has been previously explored in statistical pattern recognition with successful results. In this paper, two length models along with two parameter estimation methods and two alternative parametrisations for statistical machine translation (SMT) are presented. More precisely, we incorporate explicit bilingual length modelling in a state-of-the-art log-linear SMT system as an additional feature function in order to prove the contribution of length information. Finally, a systematic evaluation on reference SMT tasks considering different language pairs proves the benefits of explicit length modelling.

► Development of novel phrase-length models in statistical machine translation (SMT). ► Proposal of parameter estimation methods and parametrisations for these models. ► Analysis and discussion of the performance of phrase-length models. ► Systematic comparison of estimation methods and parametrisations across languages. ► Automatic evaluation on reference tasks proved the benefits of length modelling.