Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This article presents two novel ideas of improving the Machine Translation (MT) quality#R##N#by applying the word-level quality prediction for the second pass of decoding. In this#R##N#manner, the word scores estimated by Word Con
dence Estimation (WCE) systems help#R##N#to reconsider the MT hypotheses for selecting a better candidate rather than accepting#R##N#the current sub-optimal one. In the
rst attempt, the selection scope is limited to the#R##N#MT N-best list, in which our proposed re-ranking features are combined with those of the#R##N#decoder for re-scoring. Then, the search space is enlarged over the entire search graph,#R##N#storing many more hypotheses generated during the
rst pass of decoding. Over all paths#R##N#containing words of the N-best list,  propose an algorithm to strengthen or aken them#R##N#depending on the estimated word quality. In both methods, the highest-score candidate#R##N#after the search becomes the ocial translation. The results obtained show that both#R##N#approaches advance the MT quality over the one-pass baseline, and the Search Graph#R##N#Re-decoding achieves more gains (in BLEU score) than N-best List Re-ranking method.
['Ngoc Quang Luong', 'Laurent Besacier', 'Benjamin Lecouteux']
I am writing a research paper on a idea Find The Errors, Get The Better: Enhancing Machine Translation via Word Confidence Estimation
[{"from": "human", "value": "I am writing a research paper on a idea Find The Errors, Get The Better: Enhancing Machine Translation via Word Confidence Estimation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This article presents two novel ideas of improving the Machine Translation (MT) quality#R##N#by applying the word-level quality prediction for the second pass of decoding. In this#R##N#manner, the word scores estimated by Word Con\ndence Estimation (WCE) systems help#R##N#to reconsider the MT hypotheses for selecting a better candidate rather than accepting#R##N#the current sub-optimal one. In the\nrst attempt, the selection scope is limited to the#R##N#MT N-best list, in which our proposed re-ranking features are combined with those of the#R##N#decoder for re-scoring. Then, the search space is enlarged over the entire search graph,#R##N#storing many more hypotheses generated during the\nrst pass of decoding. Over all paths#R##N#containing words of the N-best list,  propose an algorithm to strengthen or aken them#R##N#depending on the estimated word quality. In both methods, the highest-score candidate#R##N#after the search becomes the o\u000ecial translation. The results obtained show that both#R##N#approaches advance the MT quality over the one-pass baseline, and the Search Graph#R##N#Re-decoding achieves more gains (in BLEU score) than N-best List Re-ranking method."}]