Abstract: Automatic post-editing(APE) is a method of automatically modifying errors in machine translation,which can improve the quality of machine translation system.Currently,APE research mainly focuses on general domains.However,there is little research on APE for patent translations,which requires high translation quality due to their strong professionalism.This paper proposes an ensemble model of APE of patent translation based on the weighted distribution of translation errors.Firstly,the term weighted translation edit rate(WTER) calculation method is proposed,which introduces the concept of term probability factor in translation edit rate(TER),and improves the WTER value of samples with more term errors.Then,the proposed WTER model is used to select subsets of mistranslation,missing translation,additional tralslation and shift error samples from the training data constructed by the three machine translation systems to construct the error correction biased APE sub-model,respectively.Finally,the biased APE sub-model is corrected by the weighted distribution of translation errors.The proposed method considers the strong professionalism and numerous technical terms in patent translations.Based on the consideration of error-correction bias,it integrates multiple sub-models to balance the diversity of translation errors.Experimental results on an English-Chinese patent abstract dataset show that,compared with the three baseline systems,the proposed method improves the BLEU values by an average of 2.52,2.28,and 2.27,respectively.

Key words: Automatic post-editing, Patent translation, Distribution of translation errors, Ensemble, Translation edit rate