论文标题
过度无法奖励：同时言语翻译的长度自适应平均滞后
Over-Generation Cannot Be Rewarded: Length-Adaptive Average Lagging for Simultaneous Speech Translation
论文作者
论文摘要
同时的语音翻译（Simulst）系统旨在以最低的潜伏期生成其输出，这通常是根据平均滞后（AL）进行计算的。在本文中，我们强调，尽管采用了广泛的采用，但AL提供了与相应参考相比产生更长预测的系统的低估分数。我们还表明，这个问题具有实际相关性，因为最近的Simulst系统确实具有过度生成的趋势。作为解决方案，我们提出了LAAL（长度自适应平均滞后），这是一个修改后的度量版本，考虑到过度生成现象，并允许对未经/过度生成的系统进行公正评估。
Simultaneous speech translation (SimulST) systems aim at generating their output with the lowest possible latency, which is normally computed in terms of Average Lagging (AL). In this paper we highlight that, despite its widespread adoption, AL provides underestimated scores for systems that generate longer predictions compared to the corresponding references. We also show that this problem has practical relevance, as recent SimulST systems have indeed a tendency to over-generate. As a solution, we propose LAAL (Length-Adaptive Average Lagging), a modified version of the metric that takes into account the over-generation phenomenon and allows for unbiased evaluation of both under-/over-generating systems.
