Neural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input texts. Unfortunately, the latter condition is not met in spoken language translation, where the input is generated by an automatic speech recognition (ASR) system. In this paper, we study how to adapt a strong NMT system to make it robust to typical ASR errors. As in our application scenarios transcripts might be post-edited by human experts, we propose adaptation strategies to train a single system that can translate either clean or noisy input with no supervision on the input type. Our experimental results on a public speech translation data set show that adapting a model on a significant amount of parallel data including ASR transcripts is beneficial with test data of the same type, but produces a small degradation when translating clean text. Adapting on both clean and noisy variants of the same data leads to the best results on both input types.

本文研究如何使强NMT系统适应典型ASR错误，并提出适应策略以训练单一系统，能够在无监督输入类型的情况下翻译干净或嘈杂的输入。通过公共演讲翻译数据集的实验结果表明，对包括ASR转录本的大量并行数据进行调整对于相同类型的测试数据是有益的，但在翻译干净文本时会产生轻微恶化。 在干净和嘈杂数据的同一数据上进行调整可以在两种输入类型上产生最佳结果。

干净与嘈杂语音转录的强健神经机器翻译