Find The Errors, Get The Better: Enhancing Machine Translation via Word Confidence Estimation
Résumé
This article presents two novel ideas of improving the Machine Translation (MT) quality
by applying the word-level quality prediction for the second pass of decoding. In this
manner, the word scores estimated by Word Condence Estimation (WCE) systems help
to reconsider the MT hypotheses for selecting a better candidate rather than accepting
the current sub-optimal one. In the rst attempt, the selection scope is limited to the
MT N-best list, in which our proposed re-ranking features are combined with those of the
decoder for re-scoring. Then, the search space is enlarged over the entire search graph,
storing many more hypotheses generated during the rst pass of decoding. Over all paths
containing words of the N-best list, we propose an algorithm to strengthen or weaken them
depending on the estimated word quality. In both methods, the highest-score candidate
after the search becomes the ocial translation. The results obtained show that both
approaches advance the MT quality over the one-pass baseline, and the Search Graph
Re-decoding achieves more gains (in BLEU score) than N-best List Re-ranking method.
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