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• W3126451397 abstract "Sign languages use multiple asynchronous information channels(articulators), not just the hands but also the face and body, whichcomputational approaches often ignore. In this paper we tackle the multiarticulatory sign language translation task and propose a novel multichanneltransformer architecture. The proposed architecture allows boththe inter and intra contextual relationships between different sign articulatorsto be modelled within the transformer network itself, while alsomaintaining channel specific information. We evaluate our approach onthe RWTH-PHOENIX-Weather-2014T dataset and report competitivetranslation performance. Importantly, we overcome the reliance on glossannotations which underpin other state-of-the-art approaches, therebyremoving the need for expensive curated datasets." @default.
• W3126451397 created "2021-02-15" @default.
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• W3126451397 date "2020-01-01" @default.
• W3126451397 modified "2023-10-10" @default.
• W3126451397 title "Multi-channel Transformers for Multi-articulatory Sign Language Translation" @default.
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• W3126451397 doi "https://doi.org/10.1007/978-3-030-66823-5_18" @default.
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