  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.Motivated by human translators, 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-C
English-German and English-French speech-to-text translation tasks to show the
effectiveness of the proposed framework.The proposed SNMT method improves the
quality-latency trade-off over the state-of-the-art monotonic multihead
attention.
