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Simultaneous neural machine translation(SNMT) models start emitting thetarget sequence before they have processed the source sequence. The recentadaptive policies for SNMT use monotonic attention to perform read/writedecisions based on the partial source and target sequences. The lack ofsufficient information might cause the monotonic attention to take poorread/write decisions, which in turn negatively affects the performance of theSNMT model. On the other hand, human translators make better read/writedecisions since they can anticipate the immediate future words using linguisticinformation and domain knowledge.Motivated by human translators, in this work,we propose a framework to aid monotonic attention with an external languagemodel to improve its decisions.We conduct experiments on the MuST-CEnglish-German and English-French speech-to-text translation tasks to show theeffectiveness of the proposed framework.The proposed SNMT method improves thequality-latency trade-off over the state-of-the-art monotonic multiheadattention.

Infusing Future Information into Monotonic Attention Through Language Models