Abstract
Simultaneous translation is widely useful but
remains one of the most difficult tasks in NLP.
Previous work either uses fixed-latency policies, or train a complicated two-staged model
using reinforcement learning. We propose a
much simpler single model that adds a “delay”
token to the target vocabulary, and design a
restricted dynamic oracle to greatly simplify
training. Experiments on Chinese?English
simultaneous translation show that our work
leads to flexible policies that achieve better
BLEU scores and lower latencies compared to
both fixed and RL-learned policies.