论文标题
CTC对准可改善自回归翻译
CTC Alignments Improve Autoregressive Translation
论文作者
论文摘要
Connectionist时间分类（CTC）是一种广泛使用的自动语音识别方法（ASR），可以执行有条件独立的单调比对。但是，对于翻译而言，由于任务的上下文和非单调性质，CTC表现出明显的局限性，因此在翻译质量方面落后于注意解码器方法。在这项工作中，我们认为，如果在联合CTC/注意框架中应用，CTC实际上确实有意义，其中CTC的核心特性可以抵消训练和解码过程中纯粹注意力模型的几个关键弱点。为了验证这一猜想，我们修改了最初为ASR支持文本到文本翻译（MT）和语音到文本翻译（ST）的混合CTC/注意模型（ST）。我们提出的联合CTC/注意模型在六个基准翻译任务上的表现优于纯通知基线。
Connectionist Temporal Classification (CTC) is a widely used approach for automatic speech recognition (ASR) that performs conditionally independent monotonic alignment. However for translation, CTC exhibits clear limitations due to the contextual and non-monotonic nature of the task and thus lags behind attentional decoder approaches in terms of translation quality. In this work, we argue that CTC does in fact make sense for translation if applied in a joint CTC/attention framework wherein CTC's core properties can counteract several key weaknesses of pure-attention models during training and decoding. To validate this conjecture, we modify the Hybrid CTC/Attention model originally proposed for ASR to support text-to-text translation (MT) and speech-to-text translation (ST). Our proposed joint CTC/attention models outperform pure-attention baselines across six benchmark translation tasks.
