Non-autoregressive Streaming Transformer for Simultaneous Translation
Zhengrui Ma, Shaolei Zhang, Shoutao Guo, Chenze Shao, Min Zhang, Yan Feng · 2023
Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality.However, training these models to achieve high quality while maintaining low latency often leads to a tendency for aggressive anticipation.We argue that such issue stems from the autoregressive architecture upon which most existing SiMT models are built.To address those issues, we propose non-autoregressive streaming Transformer (NAST) which comprises a unidirectional encoder and a non-autoregressive decoder with intra-chunk parallelism.We enable NAST to generate the blank token or repetitive tokens to adjust its READ/WRITE strategy flexibly, and train it to maximize the nonmonotonic latent alignment with an alignmentbased latency loss.Experiments on various SiMT benchmarks demonstrate that NAST outperforms previous strong autoregressive SiMT baselines.