  Lexically constrained neural machine translation (LCNMT), which controls the
translation generation with pre-specified constraints, is important in many
practical applications. Current approaches to LCNMT typically assume that the
pre-specified lexical constraints are contextually appropriate. This assumption
limits their application to real-world scenarios where a source lexicon may
have multiple target constraints, and disambiguation is needed to select the
most suitable one. In this paper, we propose disambiguated LCNMT (D-LCNMT) to
solve the problem. D-LCNMT is a robust and effective two-stage framework that
disambiguates the constraints based on contexts at first, then integrates the
disambiguated constraints into LCNMT. Experimental results show that our
approach outperforms strong baselines including existing data augmentation
based approaches on benchmark datasets, and comprehensive experiments in
scenarios where a source lexicon corresponds to multiple target constraints
demonstrate the constraint disambiguation superiority of our approach.
