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.This research was partially supported by research grant Adavoice PID2019-107579RB-I00 / AEI / 10.13039/501100011033, research grants PRE2020-094223, PID2021-126248OB-I00 and PID2019-107255GB-C21 and by Generalitat de Catalunya (AGAUR) under grant agreement 2021-SGR-00478.Peer ReviewedPostprint (published version
