A Document-Level Machine Translation Quality Estimation Model Based on Centering Theory

Machine translation Quality Estimation (QE) aims to estimate the quality of machine translations without relying on golden references. Current QE researches mainly focus on sentence-level QE models, which could not capture discourse-related translation errors. To tackle this problem, this paper presents a novel document-level QE model based on Centering Theory (CT), which is a linguistics theory for assessing discourse coherence. Furthermore, we construct and release an open-source Chinese-English corpus at https://github.com/ydc/cpqe for document-level machine translation QE, which could be used to support further studies. Finally, experimental results show that the proposed model significantly outperformed the baseline model.

Automated summary

This paper introduces a novel document-level machine translation Quality Estimation (QE) model based on Centering Theory (CT), and releases an open-source Chinese-English corpus for document-level machine translation QE. Experimental results demonstrate that the proposed model outperforms the baseline model significantly.