@inproceedings{roh:24052:sign-lang:lrec,
  author    = {Roh, Kyunggeun and Lee, Huije and Hwang, Eui Jun and Cho, Sukmin and Park, Jong C.},
  title     = {Preprocessing Mediapipe Keypoints with Keypoint Reconstruction and Anchors for Isolated Sign Language Recognition},
  pages     = {323--334},
  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/24052.html},
  abstract  = {Isolated Sign Language Recognition (ISLR) aims to classify signs into the corresponding gloss, but it remains challenging due to rapid movements and minute changes of hands. Pose-based approaches, recently gaining attention due to their robustness against the environment, are crucial against such challenging movements and changes due to the diculty of capturing small joint movements from the noisy keypoints. In this work, we emphasize the importance of preprocessing keypoints to alleviate the risk of such errors. We employ normalization using anchor points to accurately track the relative motion of skeletal joints, focusing on hand movements. Additionally, we implement bilinear interpolation to reconstruct keypoints, particularly to retrieve missing information for hands that were not detected. Preprocessing methods proposed in this work show a 6.05{\%} improvement in accuracy and achieved 83.26{\%} accuracy with data augmentation on the WLASL dataset, which is the highest among pose-based approaches. The proposed methods show strengths in cases with signs having importance in the hand shape, especially when some frames have undetected hands.}
}

@inproceedings{kim-etal-2024-signbleu:lrec,
  author    = {Kim, Jung-Ho and Huerta-Enochian, Mathew and Ko, Changyong and Lee, Du Hui},
  title     = {SignBLEU: Automatic Evaluation of Multi-channel Sign Language Translation},
  pages     = {14796--14811},
  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.1289},
  abstract  = {Sign languages are multi-channel languages that communicate information through not just the hands (manual signals) but also facial expressions and upper body movements (non-manual signals). However, since automatic sign language translation is usually performed by generating a single sequence of glosses, researchers eschew non-manual and co-occurring manual signals in favor of a simplified list of manual glosses. This can lead to significant information loss and ambiguity. In this paper, we introduce a new task named multi-channel sign language translation (MCSLT) and present a novel metric, SignBLEU, designed to capture multiple signal channels. We validated SignBLEU on a system-level task using three sign language corpora with varied linguistic structures and transcription methodologies and examined its correlation with human judgment through two segment-level tasks. We found that SignBLEU consistently correlates better with human judgment than competing metrics. To facilitate further MCSLT research, we report benchmark scores for the three sign language corpora and release the source code for SignBLEU at https://github.com/eq4all-projects/SignBLEU.}
}