This article introduces the VISTA-SL project, which aims to create an integrated e-learning platform for four European sign languages: German Sign Language, Greek Sign Language, Irish Sign Language, and Dutch Sign Language. Designed as a complement to face-to-face classes, the VISTA-SL platform will combine expertise in sign language education and education technologies to provide an adaptive and interactive learning environment suitable for deaf, hard of hearing and hearing users seeking to learn a sign language, whether it constitutes their first language or not. Building on a co-ordinated curriculum that covers vocabulary, grammar and Deaf culture materials, the platform will provide video material presented by deaf L1 signers, together with games and gamification features to motivate learning, while also providing several assistive technologies. By leveraging cutting edge language processing and computer vision approaches, the platform will provide augmented reality feedback, 3D avatars and an LLM-based virtual instructor, as part of the learning environment. VISTA-SL is developed in collaboration with end-user focus groups, comprising deaf, hard of hearing and hearing individuals. This will serve to ensure that the educational platform aligns with the expectations and needs of its intended users.

Automatic annotation of sign language data is critical for advancing linguistic research and developing sign language technologies, yet it remains a major bottleneck due to the inherently motion-based and multi-modal nature of signing. Irish Sign Language, like many sign languages, presents challenges for computational annotation and sign language processing due to limited annotated corpora and the inherent difficulty of reliably annotating movement, trajectories, and coarticulation across manual and non-manual articulators. This paper presents an automated computational framework for gloss-level annotation support in Irish Sign Language, designed to assist scalable corpus annotation by learning motion-related cues directly from sign language videos. Using ELAN-aligned segments from the Signs of Ireland Corpus, we compare contemporary self-supervised visual representations with traditional pose-based features derived from explicit skeletal tracking, evaluating three feature configurations: DINOv2, MediaPipe, and multi-modal fusion. Our results show that self-supervised visual embeddings achieve the highest average accuracy 86.12%, outperforming both multi-modal fusion 84.28% and pose-based representations 76.74%. This indicates that recent visual models can implicitly encode linguistically relevant motion information, including articulator movement and transitional dynamics, reducing the need for explicit landmark extraction in practical annotation pipelines. Overall, this work provides empirical guidance and a deployable computational framework to support computational annotation and enrichment of sign language corpora.

SignON, a 3-year Horizon 20202 project addressing the lack of technology and services for MT between sign languages (SLs) and spoken languages (SpLs) ended in December 2023. SignON was unprecedented. Not only it addressed the wider complexity of the aforementioned problem – from research and development of recognition, translation and synthesis, through development of easy-to-use mobile applications and a cloud-based framework to do the “heavy lifting” as well as to establishing ethical, privacy and inclusivenesspolicies and operation guidelines – but also engaged with the deaf and hard of hearing communities in an effective co-creation approach where these main stakeholders drove the development in the right direction and had the final say.Currently we are witnessing advances in natural language processing for SLs, including MT. SignON was one of the largest projects that contributed to this surge with 17 partners and more than 60 consortium members, working in parallel with other international and European initiatives, such as project EASIER and others.

SignON (https://signon-project.eu/) is a Horizon 2020 project, running from 2021 until the end of 2023, which addresses the lack of technology and services for the automatic translation between sign languages (SLs) and spoken languages, through an inclusive, human-centric solution, hence contributing to the repertoire of communication media for deaf, hard of hearing (DHH) and hearing individuals. In this paper, we present an update of the status of the project, describing the approaches developed to address the challenges and peculiarities of SL machine translation (SLMT).

This work is part of ongoing research work that focuses on the linguistic analysis and computational description of five different Sign Languages (SLs), namely Irish Sign Language (ISL), Flemish Sign Language (VGT), Dutch Sign Language (NGT), Spanish Sign Language (LSE), and British Sign Language (BSL). This work will be leveraged to inform the development of SL lexicon entries for a Sign Language Machine Translation (SLMT) system. In particular, this research focuses on ISL. We investigate the existence of constructions similar to or equivalent in functionality to gerunds in spoken language, in particular, English. The initial findings indicate that such constructions do indeed exist and that they can take many forms.

We present work dealing with a Linked Open Data (LOD)-compliant representation of Sign Language (SL) data, with the goal of supporting the cross-lingual alignment of SL data and their linking to Spoken Language (SpL) data. The proposed representation is based on activities of groups of researchers in the field of SL who have investigated the use of Open Multilingual Wordnet (OMW) datasets for (manually) cross-linking SL data or for linking SL and SpL data. Another group of researchers is proposing an XML encoding of articulatory elements of SLs and (manually) linking those to an SpL lexical resource. We propose an RDF-based representation of those various data. This unified formal representation offers a semantic repository of information on SL and SpL data that could be accessed for supporting the creation of datasets for training or evaluating NLP applications dealing with SLs, thinking for example of Machine Translation (MT) between SLs and between SLs and SpLs.

Natural language processing and the machine translation of spoken language (speech/text) has benefitted from significant scientific research and development in re-cent times, rapidly advancing the field. On the other hand, computational processing and modelling of signed language has unfortunately not garnered nearly as much interest, with sign languages generally being excluded from modern language technologies. Many deaf and hard-of-hearing individuals use sign language on a daily basis as their first language. For the estimated 72 million deaf people in the world, the exclusion of sign languages from modern natural language processing and machine translation technology, aggravates further the communication barrier that already exists for deaf and hard-of-hearing individuals. This research leverages a linguistically informed approach to the processing and modelling of signed language. We outline current challenges for sign language machine translation from both a linguistic and a technical prespective. We provide an account of our work in progress in the development of sign language lexicon entries and sign language lexeme repository entries for SLMT. We leverage Role and Reference Grammar together with the Sign_A computational framework with-in this development. We provide an XML description for Sign_A, which is utilised to document SL lexicon entries together with SL lexeme repository entries. This XML description is also leveraged in the development of an extension to Bahavioural Markup Language, which will be used within this development to link the divide be-tween the sign language lexicon and the avatar animation interface.

This paper addresses the tasks of sign segmentation and segment-meaning mapping in the context of sign language (SL) recognition. It aims to give an overview of the linguistic properties of SL, such as coarticulation and simultaneity, which make these tasks complex. A better understanding of SL structure is the necessary ground for the design and development of SL recognition and segmentation methodologies, which are fundamental for machine translation of these languages. Based on this preliminary exploration, a proposal for mapping segments to meaning in the form of an agglomerate of lexical and non-lexical information is introduced.