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"Translation"
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Text2Sign: Towards Sign Language Production Using Neural Machine Translation and Generative Adversarial Networks
by
Cihan, Camgoz Necati
,
Stoll, Stephanie
,
Bowden, Richard
in
Annotations
,
Generative adversarial networks
,
Gloss
2020
We present a novel approach to automatic Sign Language Production using recent developments in Neural Machine Translation (NMT), Generative Adversarial Networks, and motion generation. Our system is capable of producing sign videos from spoken language sentences. Contrary to current approaches that are dependent on heavily annotated data, our approach requires minimal gloss and skeletal level annotations for training. We achieve this by breaking down the task into dedicated sub-processes. We first translate spoken language sentences into sign pose sequences by combining an NMT network with a Motion Graph. The resulting pose information is then used to condition a generative model that produces photo realistic sign language video sequences. This is the first approach to continuous sign video generation that does not use a classical graphical avatar. We evaluate the translation abilities of our approach on the PHOENIX14T Sign Language Translation dataset. We set a baseline for text-to-gloss translation, reporting a BLEU-4 score of 16.34/15.26 on dev/test sets. We further demonstrate the video generation capabilities of our approach for both multi-signer and high-definition settings qualitatively and quantitatively using broadcast quality assessment metrics.
Journal Article
Translating in Theory and Action: Contemporary Contexts in Translation
2021
The guest editors' objective was to open up a space for researchers to reflect on and rethink the role of different categories of translation and interpreting in contemporary contexts, engaging with both theory and practice.
Journal Article