  Sign language translation as a kind of technology with profound social
significance has attracted growing researchers' interest in recent years.
However, the existing sign language translation methods need to read all the
videos before starting the translation, which leads to a high inference latency
and also limits their application in real-life scenarios. To solve this
problem, we propose SimulSLT, the first end-to-end simultaneous sign language
translation model, which can translate sign language videos into target text
concurrently. SimulSLT is composed of a text decoder, a boundary predictor, and
a masked encoder. We 1) use the wait-k strategy for simultaneous translation.
2) design a novel boundary predictor based on the integrate-and-fire module to
output the gloss boundary, which is used to model the correspondence between
the sign language video and the gloss. 3) propose an innovative re-encode
method to help the model obtain more abundant contextual information, which
allows the existing video features to interact fully. The experimental results
conducted on the RWTH-PHOENIX-Weather 2014T dataset show that SimulSLT achieves
BLEU scores that exceed the latest end-to-end non-simultaneous sign language
translation model while maintaining low latency, which proves the effectiveness
of our method.
