SignBLEU: Automatic Evaluation of Multi-channel Sign Language Translation

Jung-Ho Kim, Mathew Huerta-Enochian, Changyong Ko, Du Hui Lee


Abstract
Sign languages are multi-channel languages that communicate information through not just the hands (manual signals) but also facial expressions and upper body movements (non-manual signals). However, since automatic sign language translation is usually performed by generating a single sequence of glosses, researchers eschew non-manual and co-occurring manual signals in favor of a simplified list of manual glosses. This can lead to significant information loss and ambiguity. In this paper, we introduce a new task named multi-channel sign language translation (MCSLT) and present a novel metric, SignBLEU, designed to capture multiple signal channels. We validated SignBLEU on a system-level task using three sign language corpora with varied linguistic structures and transcription methodologies and examined its correlation with human judgment through two segment-level tasks. We found that SignBLEU consistently correlates better with human judgment than competing metrics. To facilitate further MCSLT research, we report benchmark scores for the three sign language corpora and release the source code for SignBLEU at https://github.com/eq4all-projects/SignBLEU.
Anthology ID:
2024.lrec-main.1289
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
14796–14811
Language:
URL:
https://aclanthology.org/2024.lrec-main.1289
DOI:
Bibkey:
Cite (ACL):
Jung-Ho Kim, Mathew Huerta-Enochian, Changyong Ko, and Du Hui Lee. 2024. SignBLEU: Automatic Evaluation of Multi-channel Sign Language Translation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14796–14811, Torino, Italia. ELRA and ICCL.
Cite (Informal):
SignBLEU: Automatic Evaluation of Multi-channel Sign Language Translation (Kim et al., LREC-COLING 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.lrec-main.1289.pdf