Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?
This addresses the issue of untrustworthy NMT for users by enabling self-assessment of translation quality, though it is an incremental improvement over existing quality estimation methods.
The paper tackles the problem of neural machine translation lacking awareness of its own translation quality by proposing a competency-aware NMT model with a self-estimator that reconstructs source semantics. The model achieves a higher correlation with human judgments than methods like BLEURT and COMET without needing reference or annotated data.
Neural machine translation (NMT) is often criticized for failures that happen without awareness. The lack of competency awareness makes NMT untrustworthy. This is in sharp contrast to human translators who give feedback or conduct further investigations whenever they are in doubt about predictions. To fill this gap, we propose a novel competency-aware NMT by extending conventional NMT with a self-estimator, offering abilities to translate a source sentence and estimate its competency. The self-estimator encodes the information of the decoding procedure and then examines whether it can reconstruct the original semantics of the source sentence. Experimental results on four translation tasks demonstrate that the proposed method not only carries out translation tasks intact but also delivers outstanding performance on quality estimation. Without depending on any reference or annotated data typically required by state-of-the-art metric and quality estimation methods, our model yields an even higher correlation with human quality judgments than a variety of aforementioned methods, such as BLEURT, COMET, and BERTScore. Quantitative and qualitative analyses show better robustness of competency awareness in our model.