@inproceedings{battisti:24025:sign-lang:lrec,
  author    = {Battisti, Alessia and van den Bold, Emma and G{\"o}hring, Anne and Holzknecht, Franz and Ebling, Sarah},
  title     = {Person Identification from Pose Estimates in Sign Language},
  pages     = {13--25},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources},
  maintitle = {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       = {25},
  month     = may,
  year      = {2024},
  isbn      = {978-2-493814-30-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24025.html},
  abstract  = {Sign language recognition models require extensive training data. Effectively anonymizing such data remains a complex endeavor due to the crucial role of facial features. While pose estimation techniques have traditionally been considered a means of yielding anonymized data, the findings reported in this paper challenge this assumption: We conducted a study involving Swiss German Sign Language (DSGS) users, presenting them with pose estimates from DSGS video samples. The participants' task was to identify the signers' language levels and identities from skeletal representations. Our findings reveal that the extent to which sign language users were capable of recognizing familiar signers depended on their language level, with deaf experts achieving the highest accuracy. We demonstrate that an automatic classifier obtains comparable results in multi-label language level recognition (F1=0.64) and person identification (F1=0.31). This emphasizes the need to reconsider the fundamentals of video anonymization towards guaranteeing sign language users' privacy.}
}

@inproceedings{walsh-etal-2024-select:lrec,
  author    = {Walsh, Harry and Saunders, Ben and Bowden, Richard},
  title     = {Select and Reorder: A Novel Approach for Neural Sign Language Production},
  pages     = {14531--14542},
  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.1266},
  abstract  = {Sign languages, often categorised as low-resource languages, face significant challenges in achieving accurate translation due to the scarcity of parallel annotated datasets. This paper introduces Select and Reorder (S{\&}R), a novel approach that addresses data scarcity by breaking down the translation process into two distinct steps: Gloss Selection (GS) and Gloss Reordering (GR). Our method leverages large spoken language models and the substantial lexical overlap between source spoken languages and target sign languages to establish an initial alignment. Both steps make use of Non-AutoRegressive (NAR) decoding for reduced computation and faster inference speeds. Through this disentanglement of tasks, we achieve state-of-the-art BLEU and Rouge scores on the Meine DGS Annotated (mDGS) dataset, demonstrating a substantial BLUE-1 improvement of 37.88{\%} in Text to Gloss (T2G) Translation. This innovative approach paves the way for more effective translation models for sign languages, even in resource-constrained settings.}
}

@inproceedings{jiang-etal-2024-swisssli:lrec,
  author    = {Jiang, Zifan and G{\"o}hring, Anne and Moryossef, Amit and Sennrich, Rico and Ebling, Sarah},
  title     = {{SwissSLi}: the Multi-parallel Sign Language Corpus for {Switzerland}},
  pages     = {15448--15456},
  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.1342},
  abstract  = {In this work, we introduce SwissSLi, the first sign language corpus that contains parallel data of all three Swiss sign languages, namely Swiss German Sign Language (DSGS), French Sign Language of Switzerland (LSF-CH), and Italian Sign Language of Switzerland (LIS-CH). The data underlying this corpus originates from television programs in three spoken languages: German, French, and Italian. The programs have for the most part been translated into sign language by deaf translators, resulting in a unique, up to six-way multi-parallel dataset between spoken and sign languages. We describe and release the sign language videos and spoken language subtitles as well as the overall statistics and some derivatives of the raw material. These derived components include cropped videos, pose estimation, phrase/sign-segmented videos, and sentence-segmented subtitles, all of which facilitate downstream tasks such as sign language transcription (glossing) and machine translation. The corpus is publicly available on the SWISSUbase data platform for research purposes only under a CC BY-NC-SA 4.0 license.}
}