@inproceedings{e7826332ae6d46608cea404132498346,
title = "Better Sign Language Translation with STMC-Transformer",
abstract = "Sign Language Translation (SLT) first uses a Sign Language Recognition (SLR) system to extract sign language glosses from videos. Then, a translation system generates spoken language translations from the sign language glosses. This paper focuses on the translation system and introduces the STMC-Transformer which improves on the current state-of-the-art by over 5 and 7 BLEU respectively on gloss-to-text and video-to-text translation of the PHOENIX-Weather 2014T dataset. On the ASLG-PC12 corpus, we report an increase of over 16 BLEU. We also demonstrate the problem in current methods that rely on gloss supervision. The video-to-text translation of our STMC-Transformer outperforms translation of GT glosses. This contradicts previous claims that GT gloss translation acts as an upper bound for SLT performance and reveals that glosses are an inefficient representation of sign language. For future SLT research, we therefore suggest an end-to-end training of the recognition and translation models, or using a different sign language annotation scheme.",
author = "Kayo Yin and Jesse Read",
note = "Publisher Copyright: {\textcopyright} 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.; 28th International Conference on Computational Linguistics, COLING 2020 ; Conference date: 08-12-2020 Through 13-12-2020",
year = "2020",
month = jan,
day = "1",
doi = "10.18653/v1/2020.coling-main.525",
language = "English",
series = "COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "5975--5989",
editor = "Donia Scott and Nuria Bel and Chengqing Zong",
booktitle = "COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference",
}