Adaptive tuning for statistical machine translation (AdapT)

Abstract

In statistical machine translation systems, it is a common practice to use one set of weighting parameters in scoring the candidate translations from a source language to a target language. In this paper, we challenge the assumption that only one set of weights is sufficient to pick the best candidate translation for all source language sentences. We propose a new technique that generates a different set of weights for each input sentence. Our technique outperforms the popular tuning algorithm MERT on different datasets using different language pairs.