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：注意力机制是当前自然语言处理主流架构的核心机制，其有效性已在机器翻译相关任务中得到多维度分析。研究发现，当输入文本被音频片段替代时（如语音翻译任务），注意力信息同样能为词对齐提供有效参考。本文提出AlignAtt——一种面向同声传译的新型策略，通过利用注意力信息生成源语言与目标语言的对齐关系，从而在推理阶段为模型提供引导。基于MuST-C v1.0数据集8个语言对的实验表明，AlignAtt在离线训练模型上的表现优于现有最优同声传译策略，在BLEU值上提升2个点，同时将8种语言的延迟降低0.5至0.8秒。