WSLP-2026 hosted three shared tasks on Indian Sign Language: sentence-level translation from video or pose into English (SLT), isolated recognition over a large one-shot vocabulary (ISLR), and word/sign presence prediction in continuous sentence video (WPP). Shared tasks were hosted on Codabench. Twenty-three teams registered for translation and five submitted; thirteen registered for WPP and five submitted; twenty-two registered for recognition and nine submitted. On the lexical tracks, the systems that report usable official scores treat the problem as retrieval or temporal alignment within a pose-embedding space, often with an external exemplar bank. Translation submissions almost uniformly map pose sequences into a pretrained T5 decoder. They obtain fluent English more readily than scores that can be trusted at the current accuracy level: official BLEU can sit at zero, while chrF remains small and sensitive to output length. The edition shows that ISL can be measured by automatic evaluation metrics, but that current scores still say more about retrieval recipes and decoder fluency than about signed grammar. A later edition should mark pose-tracker failures and place native-signer judgments alongside BLEU and chrF.

We organized the Second Workshop on Sign Language Processing (WSLP-2026), co-located with EMNLP 2026 in Budapest, Hungary, on 29 October 2026. The workshop continues the programme begun at WSLP 2025: to give under-resourced sign languages a dedicated computational venue, and to treat sign language processing as a set of related problems rather than a single translation benchmark. We received 40 submissions and accepted 22 papers. This overview concentrates on the 15 accepted main-workshop research papers. Those papers cover new corpora for Catalan, Uruguayan, Icelandic, Russian, and Bangla sign languages; compact gloss-free translation; isolated recognition and fingerspelling; and a cluster of diagnostic studies on what actually transfers when a model is moved from a high-resource sign language to a low-resource one. As part of the workshop, we hosted three shared tasks on Indian Sign Language, namely translation, isolated recognition, and word/sign presence prediction, which together attracted 58 registered teams, and 19 teams actively participated. The open questions have shifted from whether sign languages can be modeled at all to how annotation budgets should be allocated, which parts of a pretrained checkpoint are worth transferring, and how to evaluate systems when automatic metrics remain only weakly correlated with grammatical correctness.

We organized the First Workshop on Sign Language Processing (WSLP 2025), co-located with IJCNLP–AACL 2025 at IIT Bombay, to bring together researchers, linguists, and members of the Deaf community and accelerate computational work on under-resourced sign languages.The workshop accepted ten papers—including two official shared-task submissions—that introduced new large-scale resources (a continuous ISL fingerspelling corpus, cross-lingual HamNoSys corpora), advanced multilingual and motion-aware translation models, explored LLM-based augmentation and glossing strategies, and presented lightweight deployable systems for regional languages such as Odia.We ran a three-track shared task on Indian Sign Language that attracted over sixty registered teams and established the first public leaderboards for sentence-level ISL-to-English translation, isolated word recognition, and word-presence prediction.By centring geographic, linguistic, and organiser diversity, releasing open datasets and benchmarks, and explicitly addressing linguistic challenges unique to visual–spatial languages, we significantly broadened the scope of sign-language processing beyond traditionally dominant European and East-Asian datasets, laying a robust foundation for inclusive, equitable, and deployable sign-language AI in the Global South.

Sign language translation remains a challenging task due to the scarcity of large-scale, sentence-aligned datasets. Prior arts have focused on various feature extraction and architectural changes to support neural machine translation for sign languages. We propose PoseStitch-SLT, a novel pre-training scheme that is inspired by linguistic-templates-based sentence generation technique. With translation comparison on two sign language datasets, How2Sign and iSign, we show that a simple transformer-based encoder-decoder architecture outperforms the prior art when considering template-generated sentence pairs in training. We achieve BLEU-4 score improvements from 1.97 to 4.56 on How2Sign and from 0.55 to 3.43 on iSign, surpassing prior state-of-the-art methods for pose-based gloss-free translation. The results demonstrate the effectiveness of template-driven synthetic supervision in low-resource sign language settings.

The consequences of a healthcare data breach can be devastating for the patients, providers, and payers. The average financial impact of a data breach in recent months has been estimated to be close to USD 10 million. This is especially significant for healthcare organizations in India that are managing rapid digitization while still establishing data governance procedures that align with the letter and spirit of the law. Computer-based systems for de-identification of personal information are vulnerable to data drift, often rendering them ineffective in cross-institution settings. Therefore, a rigorous assessment of existing de-identification against local health datasets is imperative to support the safe adoption of digital health initiatives in India. Using a small set of de-identified patient discharge summaries provided by an Indian healthcare institution, in this paper, we report the nominal performance of de-identification algorithms (based on language models) trained on publicly available non-Indian datasets, pointing towards a lack of cross-institutional generalization. Similarly, experimentation with off-the-shelf de-identification systems reveals potential risks associated with the approach. To overcome data scarcity, we explore generating synthetic clinical reports (using publicly available and Indian summaries) by performing in-context learning over Large Language Models (LLMs). Our experiments demonstrate the use of generated reports as an effective strategy for creating high-performing de-identification systems with good generalization capabilities.