@article{Nguyen-Xuan2026, 
author = {Sam Nguyen-Xuan and Han Nguyen},
title = {Gloss-Internal Graph Construction and Encoding for Sign Language Translation},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {1},
keywords = {Sign language translation, gloss-to-Text translation, gloss-internal graph, sign language gloss, transformer-based models},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.078727},
doi = {10.32604/cmc.2026.078727},
abstract = {We propose a Gloss-Internal Graph Construction and Encoding framework that represents compound glosses as directed, labeled graphs and integrates them into a Transformer via a graph-aware encoder. We evaluate our approach against Rule-Based Gloss Decomposition (RBGD) and Linear Gloss Sequence Encoding (LGSE) baselines on ASLG-PC12 and PHOENIX-2014T. Results show consistent improvements over both baselines, achieving gains of up to +3.2 BLEU-4 over LGSE and +7.0 BLEU-4 over RBGD on ASLG-PC12. On PHOENIX-2014T, our method yields gains of up to 1.9 BLEU-4 on the development set and 2.4 BLEU-4 on the test set. Ablation studies further indicate that agreement and reference edges contribute most to translation quality, that attention pooling outperforms mean pooling for graph-level aggregation, and that a single message-passing step offers a reasonable accuracy–efficiency trade-off for the compact gloss-internal graphs encountered in practice. These results suggest that explicit modeling of gloss-internal structure is a promising direction for sign language translation.}
}