Abstract：Translation quality estimation (QE) technology refers to evaluating machine translation results without reference translations. Current neural translation quality estimation models can implicitly learn the syntactic structure of the source language, but they cannot effectively capture the syntactic relationships within sentences from the perspective of linguistics. This paper proposes a method to integrate the syntactic relationship information of the source sentence into neural translation quality estimation, jointly considering the internal dependency relationships of the source language and the translation quality. Experimental results show that the syntactic feature can improve the performance of the model. Finally, we used an ensemble learning algorithm to integrate multiple other linguistic features to obtain the best performance.
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2021, Vol. 35 
