Detector-Corrector: Edit-Based Automatic Post Editing for Human Post Editing

Hiroyuki Deguchi, Masaaki Nagata, Taro Watanabe

Abstract: Post-editing is crucial in the real world because neural machine translation (NMT) sometimes makes errors.Automatic post-editing (APE) attempts to correct the outputs of an MT model for better translation quality.However, many APE models are based on sequence generation, and thus their decisions are harder to interpret for actual users.In this paper, we propose “detector–corrector”, an edit-based post-editing model, which breaks the editing process into two steps, error detection and error correction.The detector model tags each MT output token whether it should be corrected and/or reordered while the corrector model generates corrected words for the spans identified as errors by the detector.Experiments on the WMT’20 English–German and English–Chinese APE tasks showed that our detector–corrector improved the translation edit rate (TER) compared to the previous edit-based model and a black-box sequence-to-sequence APE model, in addition, our model is more explainable because it is based on edit operations.