Modeling French Sign Language: a proposal for a semantically compositional system (L18-1)
Copied to clipboard
| Challenge: | Several studies have proposed linguistic models to describe sign languages, but none have succeeded to describe the specificities of SL. |
| Approach: | They propose a linguistic approach to formalize the sign language (SL) they propose to take into account linguistic properties of the SL while respecting constraints of a modelisation process. |
| Outcome: | The proposed model takes into account linguistic properties of the sign language while respecting constraints of a modelisation process. |
Similar Papers
Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation (2022.lrec-1)
Copied to clipboard
Elise Bertin-Lemée, Annelies Braffort, Camille Challant, Claire Danet, Boris Dauriac, Michael Filhol, Emmanuella Martinod, Jérémie Segouat
| Challenge: | a new corpus of french Sign Language (LSF) data is created to support future studies on the automatic translation of written French into LSF, rendered through the animation of a virtual signer. |
| Approach: | They propose to use a French Sign Language corpus called "Rosetta-LSF" it is intended to support studies on automatic translation of written French into LSF . |
| Outcome: | The proposed corpus supports future studies on automatic translation of written French into LSF, rendered through animation of a virtual signer. |
A Low-Cost Motion Capture Corpus in French Sign Language for Interpreting Iconicity and Spatial Referencing Mechanisms (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing tools for automatic translation of sign language videos into transcribed texts are limited. |
| Approach: | They propose to use deep learning methods to circumvent the use of models in spatial referencing recognition by a 3D skeleton and a software program to capture and post-process the LSF-SHELVES corpus. |
| Outcome: | The proposed system targets iconicity and spatial referencing in french sign language . it is light-weight and low-cost to collect data from a large panel of signers . |
Dicta-Sign-LSF-v2: Remake of a Continuous French Sign Language Dialogue Corpus and a First Baseline for Automatic Sign Language Processing (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing research on automatic Sign Language Processing (SLP) has focused on recognizing lexical signs, but other gestural units like iconic structures need to be recognized. |
| Approach: | They propose a public remake of the French Sign Language part of the Dicta-Sign corpus with clean annotations and a Convolutional-Recurrent Neural Network to train and test it. |
| Outcome: | The proposed version of the publicly available SL corpus Dicta-Sign is limited to its French Sign Language part and includes lexical and non-lexical annotations over 11 hours of video recording with 35000 manual units. |
Including Signed Languages in Natural Language Processing (2021.acl-long)
Copied to clipboard
| Challenge: | Existing research in Sign Language Processing (SLP) rarely explores signed languages . authors urge adoption of an efficient tokenization method and the collection of real-world signed language data . |
| Approach: | They propose to include signed languages as a research area with high social and scientific impact . they review the limitations of current SLP models and identify the open challenges . |
| Outcome: | The proposed model should include signed languages as a research area with high social and scientific impact. |
LSF-ANIMAL: A Motion Capture Corpus in French Sign Language Designed for the Animation of Signing Avatars (2020.lrec-1)
Copied to clipboard
| Challenge: | Signing avatars are often procedurally animated, resulting in robotic and unnatural movements, which are therefore rejected by the Deaf community. |
| Approach: | They propose to use a French Sign Language corpus to create an avatar that can be edited from motion capture data to create new signs and utterances. |
| Outcome: | The proposed corpus is based on a french Sign Language (LSF) corpus composed of captured signs and sentences. |
How to Align Multiple Signed Language Corpora for Better Sign-to-Sign Translations? (2025.naacl-long)
Copied to clipboard
| Challenge: | despite the growing need for advanced signing technologies, signed language resources remain scarce. |
| Approach: | They propose a linguistically informed alignment algorithm that matches instances between signed languages . they compare similarities and differences across three signed languages to develop a model . |
| Outcome: | The proposed algorithm performs well on automatic metrics for sign-to-sign translation and generation. |
SignAlignLM: Integrating Multimodal Sign Language Processing into Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Deaf and Hard-of-Hearing (DHH) users increasingly utilize Large Language Models (LLMs), yet face significant challenges due to these models’ limited understanding of sign language grammar, multimodal sign inputs, and Deafic cultural contexts. |
| Approach: | They propose to use sign language support in LLMs to integrate sign linguistic rules and conventions into prompting and fine-tuning strategies to address the needs of DHH users. |
| Outcome: | The proposed model can be generalized interfaces for both spoken and signed languages if trained with a multitasking paradigm. |
Extending AZee with Non-manual Gesture Rules for French Sign Language (2024.lrec-main)
Copied to clipboard
| Challenge: | Currently, Sign Languages (SLs) are under-resourced and are difficult to develop. |
| Approach: | They propose to extend AZee to formally represent Sign Language discourses, but also to animate them with a virtual signer. |
| Outcome: | The proposed model allows to formally represent Sign Language discourses, but also to animate them with a virtual signer. |
WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language (2022.acl-short)
Copied to clipboard
| Challenge: | Signed Language Processing (SLP) is a major form of NLP, but has been overlooked by the NLP community. |
| Approach: | They leverage existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties. |
| Outcome: | The proposed model outperforms existing approaches on signs unobserved during training. |
Can Small Vision–Language Models Perform Sign Language Translation? (2026.findings-acl)
Copied to clipboard
| Challenge: | Vision-Language Models (VLMs) have shown strong generalization across multimodal tasks, but their capacity to handle sign language translation (SLT) remains unclear. |
| Approach: | They propose entity- and semantics-aware metrics tailored for SLT to evaluate their performance. |
| Outcome: | The proposed metrics highlight the limitations of general-purpose VLMs to SLT, unlike their applicability in other tasks. |