论文标题
通过改组提高了检索增强翻译的鲁棒性
Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions
论文作者
论文摘要
最近的一些研究报告了通过从翻译记忆（TM）中检索到模糊匹配的推理时，通过扩大推理时间的翻译来改善神经机器翻译（NMT）的急剧改善。但是，这些研究均在测试时间可用的TMS与测试集高度相关的假设下进行。我们证明，对于现有的检索增强翻译方法，使用与未使用TM的TM相比，使用与域不匹配的TM可能会导致性能要差得多。我们提出了一种简单的方法，可以在训练过程中暴露模糊匹配的NMT系统，并表明它导致了一个更耐受性（高达5.8 bleu）的系统，以与域不匹配的TMS推断。同样，当对相关TMS的建议中喂养建议时，该模型仍然具有与基线的竞争力。
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.
