Regularizing End-to-End Speech Translation with Triangular Decomposition Agreement. (arXiv:2112.10991v2 [cs.CL] UPDATED)

End-to-end speech-to-text translation (E2E-ST) is becoming increasingly
popular due to the potential of its less error propagation, lower latency, and
fewer parameters. Given the triplet training corpus $\langle speech,
transcription, translation\rangle$, the conventional high-quality E2E-ST system
leverages the $\langle speech, transcription\rangle$ pair to pre-train the
model and then utilizes the $\langle speech, translation\rangle$ pair to
optimize it further. However, this process only involves two-tuple data at each
stage, and this loose coupling fails to fully exploit the association between
triplet …

arxiv speech translation