Simultaneous translation is widely useful but remains challenging. Previous
work falls into two main categories: (a) fixed-latency policies such as Ma et
al. (2019) and (b) adaptive policies such as Gu et al. (2017). The former are
simple and effective, but have to aggressively predict future content due to
diverging source-target word order; the latter do not anticipate, but suffer
from unstable and inefficient training. To combine the merits of both
approaches, we propose a simple supervised-learning framework to learn an
adaptive policy from oracle READ/WRITE sequences generated from parallel text.
At each step, such an oracle sequence chooses to WRITE the next target word if
the available source sentence context provides enough information to do so,
otherwise READ the next source word. Experiments on German<->English show that
our method, without retraining the underlying NMT model, can learn flexible
policies with better BLEU scores and similar latencies compared to previous
work.

提出了一种简单的监督学习框架，从并行文本生成的 oracle READ / WRITE 序列中学习自适应策略来结合固定延迟策略和自适应策略的优点以实现更好的 BLEU 分数和相似的延迟，同时无需重新训练基础的神经机器翻译模型。