  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.
