GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909499323842560 |
|---|---|
| author | Shahin, Nada Ismail, Leila |
| author_facet | Shahin, Nada Ismail, Leila |
| contents | Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12223 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation Shahin, Nada Ismail, Leila Computation and Language Computer Vision and Pattern Recognition I.2.6; I.2.7; I.2.10; I.4.8; I.4.9; I.4.10 Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community. |
| title | GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation |
| topic | Computation and Language Computer Vision and Pattern Recognition I.2.6; I.2.7; I.2.10; I.4.8; I.4.9; I.4.10 |
| url | https://arxiv.org/abs/2502.12223 |