  Sign Language Translation (SLT) is a promising technology to bridge the
communication gap between the deaf and the hearing people. Recently,
researchers have adopted Neural Machine Translation (NMT) methods, which
usually require large-scale corpus for training, to achieve SLT. However, the
publicly available SLT corpus is very limited, which causes the collapse of the
token representations and the inaccuracy of the generated tokens. To alleviate
this issue, we propose ConSLT, a novel token-level \textbf{Con}trastive
learning framework for \textbf{S}ign \textbf{L}anguage \textbf{T}ranslation ,
which learns effective token representations by incorporating token-level
contrastive learning into the SLT decoding process. Concretely, ConSLT treats
each token and its counterpart generated by different dropout masks as positive
pairs during decoding, and then randomly samples $K$ tokens in the vocabulary
that are not in the current sentence to construct negative examples. We conduct
comprehensive experiments on two benchmarks (PHOENIX14T and CSL-Daily) for both
end-to-end and cascaded settings. The experimental results demonstrate that
ConSLT can achieve better translation quality than the strong baselines.
