Despite recent successes with neural models for sign language translation (SLT), translation quality still lags behind spoken languages because of the data scarcity and modality gap between sign video and text. To address both problems, we investigate strategies for cross-modality representation sharing for SLT. We propose SLTUNET, a simple unified neural model designed to support multiple SLTrelated tasks jointly, such as sign-to-gloss, gloss-to-text and sign-to-text translation. Jointly modeling different tasks endows SLTUNET with the capability to explore the cross-task relatedness that could help narrow the modality gap. In addition, this allows us to leverage the knowledge from external resources, such as abundant parallel data used for spoken-language machine translation (MT). We show in experiments that SLTUNET achieves competitive and even state-of-the-art performance on PHOENIX-2014T and CSL-Daily when augmented with MT data and equipped with a set of optimization techniques. We further use the DGS Corpus for end-to-end SLT for the first time. It covers broader domains with a significantly larger vocabulary, which is more challenging and which we consider to allow for a more realistic assessment of the current state of SLT than the former two. Still, SLTUNET obtains improved results on the DGS Corpus. Code is available at https://github.com/bzhangGo/sltunet.

为了解决手语翻译领域（SLT）中数据不足和模态差异问题，本文提出了一种简单的统一神经模型SLTUNET，并采用跨模态表示共享的策略，使其支持多个SLT相关任务的联合建模，其中包括手语翻译，手势码翻译和手语转文本翻译等。实验结果表明，SLTUNET在使用机器翻译数据和优化技术进行改进后，在PHOENIX-2014T和CSL-Daily数据集上取得了具有竞争力的甚至达到最先进的性能，也首次使用DGS语料库进行SLT，进一步证明了SLTUNET的有效性。

SLTUNET：一种简单的统一手语翻译模型