  Sign language recognition and translation first uses a recognition module to
generate glosses from sign language videos and then employs a translation
module to translate glosses into spoken sentences. Most existing works focus on
the recognition step, while paying less attention to sign language translation.
In this work, we propose a task-aware instruction network, namely TIN-SLT, for
sign language translation, by introducing the instruction module and the
learning-based feature fuse strategy into a Transformer network. In this way,
the pre-trained model's language ability can be well explored and utilized to
further boost the translation performance. Moreover, by exploring the
representation space of sign language glosses and target spoken language, we
propose a multi-level data augmentation scheme to adjust the data distribution
of the training set. We conduct extensive experiments on two challenging
benchmark datasets, PHOENIX-2014-T and ASLG-PC12, on which our method
outperforms former best solutions by 1.65 and 1.42 in terms of BLEU-4. Our code
is published at https://github.com/yongcaoplus/TIN-SLT.
