Efficient Monotonic Multihead Attention
Xutai Ma Anna Sun Siqi Ouyang Hirofumi Inaguma Paden Tomasello
We introduce the Efficient Monotonic Multihead Attention (EMMA), a
state-of-the-art simultaneous translation model with numerically-stable and
unbiased monotonic alignment estimation. In addition, we present improved
training and inference strategies, including simultaneous fine-tuning from an
offline translation model and reduction of monotonic alignment variance. The
experimental results demonstrate that the proposed model attains
state-of-the-art performance in simultaneous speech-to-text translation on the
Spanish and English translation task.