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.
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DiscoSign is a new computational framework that translates text into sign language glosses while explicitly handling discourse-level phenomena. It focuses on three key aspects: spatial coreference resolution to keep entities in consistent spatial positions, Question-Answer Clauses (QACs) that serve specific discourse functions, and concept-gloss consistency to maintain stable mappings between English concepts and ASL signs. The authors introduce novel evaluation metrics tailored to these discourse dimensions and demonstrate that DiscoSign improves spatial consistency and entity tracking compared to sentence-only translation models, without sacrificing single-sentence gloss quality.
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.
SignGPT is a unified, pose‑based framework that performs gloss‑free sign language translation (SLT) and generation (SLG) by integrating part‑aware hierarchical representations of body, hand, and facial motion into a shared language model. It uses asymmetric multi‑token prediction and progressive training for bidirectional modeling, and is evaluated on How2Sign (ASL) and Phoenix‑2014T (DGS) with benchmark comparisons, qualitative analyses, and component ablations. An exploratory study with 12 Deaf ASL signers demonstrates a sign‑to‑sign response pipeline, suggesting that unified modeling can support sign language conversation (SLC).
arXiv:2610.00881v1 Announce Type: new Abstract: Sign language machine translation has progressed substantially over the past decade, evolving from isolated sign recognition to end-to-end translation...
The paper introduces FS23K, a large-scale British Sign Language fingerspelling dataset created through an iterative annotation framework. It also presents a recognition model that incorporates bi‑manual interactions and mouthing cues, achieving a halved character error rate compared to previous state‑of‑the‑art methods. These results underscore the dataset’s and model’s value for advancing sign language research and automated annotation pipelines.
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.