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.

翻译：手语翻译（SLT）是一项有望弥合听障人士与健听人士之间沟通鸿沟的前沿技术。近年来，研究人员采用神经机器翻译（NMT）方法实现手语翻译，这类方法通常需要大规模语料库进行训练。然而，公开可用的手语翻译语料库极为有限，导致词元表示坍塌及生成词元不准确的问题。为缓解这一困境，我们提出ConSLT——一种新颖的词元级对比学习框架，通过将词元级对比学习融入手语翻译解码过程，学习有效的词元表示。具体而言，ConSLT在解码过程中将每个词元及其由不同随机失活掩码生成的对应词元视为正样本对，并从当前句子未包含的词汇表中随机采样$K$个词元构建负样本。我们在两个基准数据集（PHOENIX14T和CSL-Daily）上，针对端到端与级联两种设置开展了全面实验。实验结果表明，ConSLT相比强基线方法能够实现更优的翻译质量。