Sign languages use multiple asynchronous information channels (articulators),
not just the hands but also the face and body, which computational approaches
often ignore. In this paper we tackle the multi-articulatory sign language
translation task and propose a novel multi-channel transformer architecture.
The proposed architecture allows both the inter and intra contextual
relationships between different sign articulators to be modelled within the
transformer network itself, while also maintaining channel specific
information. We evaluate our approach on the RWTH-PHOENIX-Weather-2014T dataset
and report competitive translation performance. Importantly, we overcome the
reliance on gloss annotations which underpin other state-of-the-art approaches,
thereby removing future need for expensive curated datasets.

本文提出了一种新的多通道变压器架构，能够处理多关节手语翻译任务，同时既考虑关节之间的上下文关系，又保持通道特定信息，并证明了其具备在 RWTH-PHOENIX-Weather-2014T 数据集上竞争力的翻译性能，同时消除了对贵重高质量注释数据集的需求。