Considerations for meaningful sign language machine translation based on glosses
Mathias Müller, Zifan Jiang, Amit Moryossef, Annette Rios, Sarah Ebling
Abstract
Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021). In machine translation (MT) in particular, sign language translation based on glosses is a prominent approach. In this paper, we review recent works on neural gloss translation. We find that limitations of glosses in general and limitations of specific datasets are not discussed in a transparent manner and that there is no common standard for evaluation. To address these issues, we put forward concrete recommendations for future research on gloss translation. Our suggestions advocate awareness of the inherent limitations of gloss-based approaches, realistic datasets, stronger baselines and convincing evaluation.
BibTeX
@inproceedings{muller-etal-2023-considerations,
title = "Considerations for meaningful sign language machine translation based on glosses",
author = {M{\"u}ller, Mathias and
Jiang, Zifan and
Moryossef, Amit and
Rios, Annette and
Ebling, Sarah},
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.60/",
doi = "10.18653/v1/2023.acl-short.60",
pages = "682--693"
}