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Automating sign language translation (SLT) is a challenging real worldapplication. Despite its societal importance, though, research progress in thefield remains rather poor. Crucially, existing methods that yield viableperformance necessitate the availability of laborious to obtain gloss sequencegroundtruth. In this paper, we attenuate this need, by introducing anend-to-end SLT model that does not entail explicit use of glosses; the modelonly needs text groundtruth. This is in stark contrast to existing end-to-endmodels that use gloss sequence groundtruth, either in the form of a modalitythat is recognized at an intermediate model stage, or in the form of a paralleloutput process, jointly trained with the SLT model. Our approach constitutes aTransformer network with a novel type of layers that combines: (i) localwinner-takes-all (LWTA) layers with stochastic winner sampling, instead ofconventional ReLU layers, (ii) stochastic weights with posterior distributionsestimated via variational inference, and (iii) a weight compression techniqueat inference time that exploits estimated posterior variance to performmassive, almost lossless compression. We demonstrate that our approach canreach the currently best reported BLEU-4 score on the PHOENIX 2014T benchmark,but without making use of glosses for model training, and with a memoryfootprint reduced by more than 70%.

Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation