Accuracy-based scoring for phrase-based statistical machine translation

Although the scoring features of state-of-the-art Phrase-Based Statistical Machine Translation (PB-SMT) models are weighted so as to optimise an objective function measuring translation quality, the estimation of the features themselves does not have any relation to such quality metrics. In this paper, we introduce a translation quality-based feature to PBSMT in a bid to improve the translation quality of the system. Our feature is estimated by averaging the edit-distance between phrase pairs involved in the translation of oracle sentences, chosen by automatic evaluation metrics from the N-best outputs of a baseline system, and phrase pairs occurring in the N-best list. Using our method, we report a statistically significant 2.11% relative improvement in BLEU score for the WMT 2009 Spanish-to-English translation task. We also report that using our method we can achieve statistically significant improvements over the baseline using many other MT evaluation metrics, and a substantial increase in speed and reduction in memory use (due to a reduction in phrase-table size of 87%) while maintaining significant gains in translation quality

Accuracy-based scoring for DOT: towards direct error minimization for data-oriented translation

In this work we present a novel technique to rescore fragments in the Data-Oriented Translation model based on their contribution to translation accuracy. We describe three new rescoring methods, and present the initial results of a pilot experiment on a small subset of the Europarl corpus. This work is a proof-of-concept, and is the first step in directly optimizing translation decisions solely on the hypothesized accuracy of potential translations resulting from those decisions

Optimizing Machine Translation by Learning to Search

We present a novel approach to training discriminative tree-structured machine trans- lation systems by learning to search. We describe three primary innovations in this work: a new parsing coordinator architecture and algorithms to generate the required training examples for the learning algorithm; a new semiring that provides an unbiased way to compare translations; and a new training objective that measures whether a translation inference improves the quality of a translation. We also apply the reinforcement learning concept of exploration to SMT. Finally, we empirically evaluate our innovations

Accuracy-Based Scoring for Phrase-Based Statistical Machine Translation

Although the scoring features of state-of-theart Phrase-Based Statistical Machine Translation (PB-SMT) models are weighted so as to optimise an objective function measuring translation quality, the estimation of the features themselves does not have any relation to such quality metrics. In this paper, we introduce a translation quality-based feature to PB-SMT in a bid to improve the translation quality of the system. Our feature is estimated by averaging the edit-distance between phrase pairs involved in the translation of oracle sentences, chosen by automatic evaluation metrics from the N-best outputs of a baseline system, and phrase pairs occurring in the N-best list. Using our method, we report a statistically significant 2.11 % relative improvement in BLEU score for the WMT 2009 Spanish-to-English translation task. We also report that using our method we can achieve statistically significant improvements over the baseline using many other MT evaluation metrics, and a substantial increase in speed and reduction in memory use (due to a reduction in phrase-table size of 87%) while maintaining significant gains in translation quality