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However, current autoregressive approaches suffer from high latency. In thispaper, we focus on non-autoregressive translation (NAT) for this problem forits efficiency advantage. We identify that current constrained NAT models,which are based on iterative editing, do not handle low-frequency constraintswell. To this end, we propose a plug-in algorithm for this line of work, i.e.,Aligned Constrained Training (ACT), which alleviates this problem byfamiliarizing the model with the source-side context of the constraints.Experiments on the general and domain datasets show that our model improvesover the backbone constrained NAT model in constraint preservation andtranslation quality, especially for rare constraints.

Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints