  Lexically constrained neural machine translation (NMT), which controls the
generation of NMT models with pre-specified constraints, is important in many
practical scenarios. Due to the representation gap between discrete constraints
and continuous vectors in NMT models, most existing works choose to construct
synthetic data or modify the decoding algorithm to impose lexical constraints,
treating the NMT model as a black box. In this work, we propose to open this
black box by directly integrating the constraints into NMT models.
Specifically, we vectorize source and target constraints into continuous keys
and values, which can be utilized by the attention modules of NMT models. The
proposed integration method is based on the assumption that the correspondence
between keys and values in attention modules is naturally suitable for modeling
constraint pairs. Experimental results show that our method consistently
outperforms several representative baselines on four language pairs,
demonstrating the superiority of integrating vectorized lexical constraints.
