We introduce a reinforcement learningbased approach to simultaneous machine translation-producing a translation while receiving input wordsbetween languages with drastically different word orders: from verb-final languages (e.g., German) to verb-medial languages (English). In traditional machine translation, a translator must "wait" for source material to appear before translation begins. We remove this bottleneck by predicting the final verb in advance. We use reinforcement learning to learn when to trust predictions about unseen, future portions of the sentence. We also introduce an evaluation metric to measure expeditiousness and quality. We show that our new translation model outperforms batch and monotone translation strategies.
REF formulate simultaneous translation as a sequential decision making problem and uses reinforcement learning to decide when to translate.
Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation