Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning
This work addresses the challenge of real-time translation for applications requiring low latency, though it is incremental as it builds on existing simultaneous translation methods.
The paper tackles the problem of simultaneous machine translation by learning coupled programmer-interpreter policies using imitation learning, resulting in improved translation quality and low delay across six language pairs.
We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies. First, wepresent an algorithmic oracle to produce oracle READ/WRITE actions for training bilingual sentence-pairs using the notion of word alignments. This oracle actions are designed to capture enough information from the partial input before writing the output. Next, we perform a coupled scheduled sampling to effectively mitigate the exposure bias when learning both policies jointly with imitation learning. Experiments on six language-pairs show our method outperforms strong baselines in terms of translation quality while keeping the translation delay low.