Confidence-based Rewriting of Machine Translation Output
Benjamin Marie, Aurélien Max · 2014
Numerous works in Statistical Machine Translation (SMT) have attempted to iden-tify better translation hypotheses obtained by an initial decoding using an improved, but more costly scoring function. In this work, we introduce an approach that takes the hypotheses produced by a state-of-the-art, reranked phrase-based SMT sys-tem, and explores new parts of the search space by applying rewriting rules se-lected on the basis of posterior phrase-level confidence. In the medical do-main, we obtain a 1.9 BLEU improve-ment over a reranked baseline exploiting the same scoring function, corresponding to a 5.4 BLEU improvement over the orig-inal Moses baseline. We show that if an indication of which phrases require rewrit-ing is provided, our automatic rewriting procedure yields an additional improve-ment of 1.5 BLEU. Various analyses, in-cluding a manual error analysis, further il-lustrate the good performance and poten-tial for improvement of our approach in spite of its simplicity. 1