Minimum description length inference of phrase-based translation models

Abstract

This work explores the application of minimum description length (MDL) inference to estimate the parameters of phrase-based statistical machine translation (SMT) models. In comparison with current inference techniques that rely on a long decoupled pipeline with multiple heuristic steps, MDL is a well-founded theoretically sound approach whose empirical results are however below those of the heuristically motivated state-of-the-art training pipeline. We identify potential limitations of MDK inference when applied to natural language and propose practical approaches to overcome them when inferring SMT models. The evaluation in a Spanish-to-English translation task demonstrates that MDL inference can be adapted to yield a performance close to the state of the art.