Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning
Philip Arthur, Trevor Cohn, Gholamreza Haffari · 2021
We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies.First, we present 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.