论文标题
注意作为同时言语翻译的指南
Attention as a Guide for Simultaneous Speech Translation
论文作者
论文摘要
对注意机制的研究引发了人们对许多领域的兴趣，例如语言建模和机器翻译。尽管已利用其模式来执行不同的任务，从神经网络的理解到文本一致性，但以前没有工作分析了语音翻译（ST）中的编码器核对器注意行为，也没有用它来改善特定任务的ST。在本文中，我们通过提出基于注意力的策略（EDATT）的同时ST（Simulst）来填补这一空白，该策略是通过对音频输入和文本输出之间现有注意力关系的分析而激发的。它的目标是利用编码器注意分数实时指导推断。 EN-> {de，ES}的结果表明，与仿真状态相比，EDATT策略取得了更好的结果，尤其是在计算意识到的潜伏期方面。
The study of the attention mechanism has sparked interest in many fields, such as language modeling and machine translation. Although its patterns have been exploited to perform different tasks, from neural network understanding to textual alignment, no previous work has analysed the encoder-decoder attention behavior in speech translation (ST) nor used it to improve ST on a specific task. In this paper, we fill this gap by proposing an attention-based policy (EDAtt) for simultaneous ST (SimulST) that is motivated by an analysis of the existing attention relations between audio input and textual output. Its goal is to leverage the encoder-decoder attention scores to guide inference in real time. Results on en->{de, es} show that the EDAtt policy achieves overall better results compared to the SimulST state of the art, especially in terms of computational-aware latency.
