The advances in automatic sign language translation (SLT) to spoken languages have been mostly benchmarked with datasets of limited size and restricted domains. Our work advances the state of the art by providing the first baseline results on How2Sign, a large and broad dataset. We train a Transformer over I3D video features, using the reduced BLEU as a reference metric for validation, instead of the widely used BLEU score. We report a result of 8.03 on the BLEU score, and publish the first open-source implementation of its kind to promote further advances.

翻译：自动手语翻译（SLT）至口语语言的研究进展主要依赖于规模有限且领域受限的数据集进行基准测试。我们的工作通过首次在大型广泛数据集How2Sign上建立基线结果，推动了该领域的最新技术水平。我们使用I3D视频特征训练Transformer模型，并采用简化版BLEU（而非广泛使用的BLEU评分）作为验证参考指标。实验报告显示BLEU评分为8.03，同时我们发布了该领域的首个开源实现，以促进进一步的研究进展。