@inproceedings{battisti:24019:sign-lang:lrec,
  author    = {Battisti, Alessia and Tissi, Katja and Sidler-Miserez, Sandra and Ebling, Sarah},
  title     = {Advancing Annotation for Continuous Data in {Swiss} {German} {Sign} {Language}},
  pages     = {1--12},
  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/24019.html},
  abstract  = {This paper presents a transcription and annotation scheme introduced specifically for L1 and L2 continuous data of Swiss German Sign Language, with potential applicability to other sign languages. The scheme includes a novel way of annotating linguistic errors in L2 data, thereby contributing to a deeper understanding of sign language learning. An initial validation approach is outlined, revealing challenges and underscoring the necessity for a more comprehensive method for validating sign language (learner) data. The paper emphasizes the overarching goal of achieving interoperability among sign language corpora and research groups, particularly in advancing sign language data validation techniques.}
}

@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{walsh:70007:sltat:lrec,
  author    = {Walsh, Harry and Saunders, Ben and Bowden, Richard},
  title     = {Changing the Representation: Examining Language Representation for Neural Sign Language Production},
  pages     = {117--124},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee},
  booktitle = {Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives},
  maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {24},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-82-5},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/2022.sltat-1.18.html},
  abstract  = {Neural Sign Language Production (SLP) aims to automatically translate from spoken language sentences to sign language videos. Historically the SLP task has been broken into two steps; Firstly, translating from a spoken language sentence to a gloss sequence and secondly, producing a sign language video given a sequence of glosses. In this paper we apply Natural Language Processing techniques to the first step of the SLP pipeline. We use language models such as BERT and Word2Vec to create better sentence level embeddings, and apply several tokenization techniques, demonstrating how these improve performance on the low resource translation task of Text to Gloss. We introduce Text to HamNoSys (T2H) translation, and show the advantages of using a phonetic representation for sign language translation rather than a sign level gloss representation. Furthermore, we use HamNoSys to extract the hand shape of a sign and use this as additional supervision during training, further increasing the performance on T2H. Assembling best practise, we achieve a BLEU-4 score of 26.99 on the MineDGS dataset and 25.09 on PHOENIX14T, two new state-of-the-art baselines.}
}