Several recent studies have reported dramatic performance improvements in
neural machine translation (NMT) by augmenting translation at inference time
with fuzzy-matches retrieved from a translation memory (TM). However, these
studies all operate under the assumption that the TMs available at test time
are highly relevant to the testset. We demonstrate that for existing retrieval
augmented translation methods, using a TM with a domain mismatch to the test
set can result in substantially worse performance compared to not using a TM at
all. We propose a simple method to expose fuzzy-match NMT systems during
training and show that it results in a system that is much more tolerant
(regaining up to 5.8 BLEU) to inference with TMs with domain mismatch. Also,
the model is still competitive to the baseline when fed with suggestions from
relevant TMs.

使用翻译记忆进行模糊匹配的神经机器翻译系统，存在与测试数据不匹配的领域问题。作者提出了一种简单的方法，在训练期间暴露模糊匹配，从而使系统对与测试数据不匹配的翻译记忆具有更好的容错性，并且该模型在使用相关翻译记忆的情况下仍然具有竞争力。