A Fine-Grained Word-level Translation Quality Estimation Method based on Deep Learning
2023 3rd International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT)(2023)
摘要
Translation quality estimation (QE) technology aims to evaluate the quality of machine translations without reference translations. Currently, research on word-level QE mainly focuses on discriminating the correctness of word translations in sentences. In order to further identify the types of errors in the translation, this paper proposes a fine-grained word-level QE method. A new translation error taxonomy and a method to automatically generate labelled training corpus are proposed. Two methods based on recurrent neural network BiLSTM and pre-trained model XLM-R are adopted to build a model on the automatically generated corpus, and weighted cross-entropy loss function is used to alleviate the problem of label imbalance. Experimental results show that the proposed methods can effectively identify the fine-grained errors in the translation, which provides more information for post-editors.
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关键词
Word-level translation quality estimation,BiLSTM,XLM-R,Error classification taxonomy
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