  This paper presents Self-correcting Encoding (Secoco), a framework that
effectively deals with input noise for robust neural machine translation by
introducing self-correcting predictors. Different from previous robust
approaches, Secoco enables NMT to explicitly correct noisy inputs and delete
specific errors simultaneously with the translation decoding process. Secoco is
able to achieve significant improvements over strong baselines on two
real-world test sets and a benchmark WMT dataset with good interpretability. We
will make our code and dataset publicly available soon.
