We present Grid Beam Search (GBS), an algorithm which extends beam search to
allow the inclusion of pre-specified lexical constraints. The algorithm can be
used with any model that generates a sequence $ \mathbf{\hat{y}} =
\{y_{0}\ldots y_{T}\} $, by maximizing $ p(\mathbf{y} | \mathbf{x}) =
\prod\limits_{t}p(y_{t} | \mathbf{x}; \{y_{0} \ldots y_{t-1}\}) $. Lexical
constraints take the form of phrases or words that must be present in the
output sequence. This is a very general way to incorporate additional knowledge
into a model's output without requiring any modification of the model
parameters or training data. We demonstrate the feasibility and flexibility of
Lexically Constrained Decoding by conducting experiments on Neural
Interactive-Predictive Translation, as well as Domain Adaptation for Neural
Machine Translation. Experiments show that GBS can provide large improvements
in translation quality in interactive scenarios, and that, even without any
user input, GBS can be used to achieve significant gains in performance in
domain adaptation scenarios.

介绍了 Grid Beam Search 算法，可以在任何生成序列的模型中使用，通过词汇约束来扩展 beam 搜索，并且在进行交互式预测翻译和领域自适应翻译实验中表现出良好的性能提升。