@inproceedings{holmes-etal-2024-keypoints:lrec, author = {Holmes, Ruth and Rushe, Ellen and Ventresque, Anthony}, title = {The Key Points: Using Feature Importance to Identify Shortcomings in Sign Language Recognition Models}, pages = {15970--15975}, editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen}, booktitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)}, publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}}, address = {Torino, Italy}, day = {20--25}, month = may, year = {2024}, isbn = {978-2-493814-10-4}, language = {english}, url = {https://aclanthology.org/2024.lrec-main.1387}, abstract = {Pose estimation keypoints are widely used in sign language recognition (SLR) as a means of generalising to unseen signers. Despite the advantages of keypoints, SLR models struggle to achieve high recognition accuracy for many signed languages due to the large degree of variability between occurrences of the same signs, the lack of large datasets and the imbalanced nature of the data therein. In this paper we seek to provide a deeper analysis into the ways that these keypoints are used by models in order to determine which are most informative to SLR, identify potentially redundant ones and investigate whether keypoints that are central to differentiating signs in practice are being effectively used as expected by models.} } @inproceedings{brosens:22002:sign-lang:lrec, author = {Brosens, Caro and Janssens, Margot and Verstraete, Sam and Vandamme, Thijs and De Durpel, Hannes}, title = {Moving towards a Functional Approach in the {Flemish} {Sign} {Language} Dictionary Making Process}, pages = {24--28}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC2022} 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources}, maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {25}, month = jun, year = {2022}, isbn = {979-10-95546-86-3}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/22002.html}, abstract = {This presentation will outline the dictionary making process of the new online Flemish Sign Language dictionary launched in 2019. First some necessary background information is provided, consisting of a brief history of Flemish Sign Language (VGT) lexicography. Then three phases in the development of the renewed dictionary of VGT will be explored: (i) user research, (ii) data-cleaning and modeling, and (iii) innovations. More than wanting to project a report of lexicographic research on a website, the goal was to make the new dictionary a practical, user-friendly reference tool that meets the needs, expectations, and skills of the dictionary users. To gain a better understanding of who the users were, several sources were consulted: the user research by Joni Oyserman (2013), the quantitative data from Google Analytics and VGTC's own user profiles. Since 2017, VGTC has been using Signbank, an electronic database specifically developed to compile and manage lexicographic data for sign languages. Bringing together all this raw data inadvertently led to inconsistencies and small mistakes, therefore the data had to be manually revised and complemented. The VGT dictionary was mainly formally modernized, but there are also several substantive differences regarding the previous dictionary: for instance, search options were expanded, and semantic categories were added as well as a new feedback feature. In addition, the new website is also structurally different, it is now responsive to all screen sizes. Lastly, possible future innovations will briefly be discussed. VGTC aims to continuously improve both the user-based interface and the content of the current dictionary. Future goals include, but are not limited to, adding definitions and sample sentences (preferably extracted from the corpus), as well as information on the etymology and common use of signs.} } @inproceedings{de-coster-etal-2020-sign:lrec, author = {De Coster, Mathieu and Van Herreweghe, Mieke and Dambre, Joni}, title = {Sign Language Recognition with Transformer Networks}, pages = {6018--6024}, editor = {Calzolari, Nicoletta and Fr{\'e}d{\'e}ric B{\'e}chet and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios}, booktitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {11--16}, month = may, year = {2020}, isbn = {979-10-95546-34-4}, language = {english}, url = {https://aclanthology.org/2020.lrec-1.737}, abstract = {Sign languages are complex languages. Research into them is ongoing, supported by large video corpora of which only small parts are annotated. Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid research into sign languages and sign language recognition. Previous research has approached sign language recognition in various ways, using feature extraction techniques or end-to-end deep learning. In this work, we apply a combination of feature extraction using OpenPose for human keypoint estimation and end-to-end feature learning with Convolutional Neural Networks. The proven multi-head attention mechanism used in transformers is applied to recognize isolated signs in the Flemish Sign Language corpus. Our proposed method significantly outperforms the previous state of the art of sign language recognition on the Flemish Sign Language corpus: we obtain an accuracy of 74.7{\%} on a vocabulary of 100 classes. Our results will be implemented as a suggestion system for sign language corpus annotation.} } @inproceedings{pigou:16011:sign-lang:lrec, author = {Pigou, Lionel and Van Herreweghe, Mieke and Dambre, Joni}, title = {Sign Classification in Sign Language Corpora with Deep Neural Networks}, pages = {175--178}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna}, booktitle = {Proceedings of the {LREC2016} 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining}, maintitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Portoro{\v z}, Slovenia}, day = {28}, month = may, year = {2016}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/16011.html}, abstract = {Automatic and unconstrained sign language recognition (SLR) in image sequences remains a challenging problem. The variety of signers, backgrounds, sign executions and signer positions makes the development of SLR systems very challenging. Current methods try to alleviate this complexity by extracting engineered features to detect hand shapes, hand trajectories and facial expressions as an intermediate step for SLR. Our goal is to approach SLR based on feature learning rather than feature engineering. We tackle SLR using the recent advances in the domain of deep learning with deep neural networks. The problem is approached by classifying isolated signs from the Corpus VGT (Flemish Sign Language Corpus) and the Corpus NGT (Dutch Sign Language Corpus). Furthermore, we investigate cross-domain feature learning to boost the performance to cope with the fewer Corpus VGT annotations.} }