Title: Infusing Future Information into Monotonic Attention Through Language Models. Abstract: Simultaneous neural machine translation (SNMT) models start emitting the target sequence before they have processed the source sequence.  The recent adaptive policies for SNMT use monotonic attention to perform read/write decisions based on  the  partial  source  and  target  sequences.   The  lack  of  sufficient  information might cause the monotonic attention to take poor read/write decisions, which in turn negatively affects the performance of the SNMT model.  On the other hand, human translators make better read/write decisions since they can anticipate the immediate future words using linguistic information and domain knowledge.  In this work, we propose a framework to aid monotonic attention with an external language model to improve its decisions. We conduct experiments on the MuST-CEnglish-German and English-French speech-to-text translation tasks to show the effectiveness of the proposed framework. It improves the quality-latency trade-off over the state-of-the-art monotonic multihead attention.