DiscoSign is a discourse-aware text-to-sign-language gloss translation system that builds on a modular Large Language Model framework. It tackles three key discourse phenomena: spatial coreference resolution, Question-Answer Clauses (QACs), and concept-gloss consistency. The authors introduce new evaluation metrics and demonstrate that DiscoSign improves spatial consistency and entity tracking compared to sentence-only translation while maintaining competitive single-sentence gloss quality.
arXiv:2606. 08056v1 Announce Type: cross Abstract: Sign language models are predominantly trained with gloss-sequence or text supervision, thereby under-modeling non-lexical and productive constructions.
By Oline Ranum, Simon Hadfield, Richard Bowden
arXiv:2605. 31393v2 Announce Type: replace-cross Abstract: Sign language translation (SLT) remains constrained by the limited availability of paired sign-video/text corpora and by the heavy-tailed vocabularies typical of real-world datasets.
By Pedro Dal Bianco, Jean Paul Nunes Reinhold, Oscar Stanchi, Facundo Quiroga, Franco Ronchetti, Ulisses Brisolara Corr\^ea
SignRAG is a unified framework for gloss‑free sign language translation that combines hierarchical pretraining, target‑domain retrieval augmentation, and retrieval‑aware reinforcement fine‑tuning. It first learns linguistically grounded sign representations, aligns a sign encoder with a decoder‑only large language model, and then uses a retrieval gallery to provide instance‑specific translation cues. Retrieval Utility‑Guided Reinforcement Fine‑Tuning (RUG‑RFT) balances translation quality and retrieval usefulness, leading to state‑of‑the‑art results, including the first gloss‑free approach to outperform gloss‑supervised methods on CSL‑Daily.
By Zhi Rao, Yucheng Zhou, Qianran Sun, Yiqing Huang, Longcan Yuan, Jiayi Hou, Chengwen Yao, Lin Cheng, Donghui Sun, Xiaoxin Chen, Jun Wan
The paper argues that BLEU-4, the prevailing metric for sign language translation (SLT), may not accurately reflect sign language proficiency because SLT models can exploit spurious correlations and spoken-language priors. By evaluating six SLT models on Phoenix-2014T and CSL-Daily, the authors show that higher BLEU-4 scores do not necessarily indicate better spatio-temporal understanding. They propose a new open-weight LLM QA protocol inspired by language-learning assessment, which better preserves salient content, aligns more closely with human rankings, and reveals differences between gloss-free and gloss-supervised systems that BLEU-4 obscures.
By Oline Ranum, Edward Fish, Simon Hadfield, Richard Bowden
arXiv:2608. 09045v1 Announce Type: cross Abstract: Recent advances in sign language (SL) research have shown a trend toward unifying multiple sign language understanding (SLU) subtasks, such as isolated sign language recognition (ISLR), continuous sign language recognition (CSLR), and sign language translation (SLT), within a single framework, leading to substantial progress.
By Xiao Liu, Shiwei Gan, Yafeng Yin, Jiaxin Yin, Bowen Guo, Yaqi Sun, Zhiwei Jiang, Lei Xie