Attention is the core mechanism of today's most used architectures for natural language processing and has been analyzed from many perspectives, including its effectiveness for machine translation-related tasks. Among these studies, attention resulted to be a useful source of information to get insights about word alignment also when the input text is substituted with audio segments, as in the case of the speech translation (ST) task. In this paper, we propose AlignAtt, a novel policy for simultaneous ST (SimulST) that exploits the attention information to generate source-target alignments that guide the model during inference. Through experiments on the 8 language pairs of MuST-C v1.0, we show that AlignAtt outperforms previous state-of-the-art SimulST policies applied to offline-trained models with gains in terms of BLEU of 2 points and latency reductions ranging from 0.5s to 0.8s across the 8 languages.

翻译：注意力机制是当今最主流的自然语言处理架构的核心机制，已从多个角度被分析，包括其在机器翻译相关任务中的有效性。在这些研究中，注意力被证明是获取词对齐信息的有用来源，即使在输入文本被替换为音频片段的情况下，如语音翻译任务。本文提出AlignAtt，一种针对同声传译的新型策略，利用注意力信息生成源语言-目标语言对齐，从而在推理过程中指导模型。通过在MuST-C v1.0的8个语言对上的实验，我们证明AlignAtt优于以往应用于离线训练模型的最先进同声传译策略，在8个语言上实现了2个BLEU点的提升以及0.5秒至0.8秒的延迟降低。