@inproceedings{kim:24028:sign-lang:lrec,
  author    = {Kim, Jung-Ho and Ko, Changyong and Huerta-Enochian, Mathew and Ko, Seung Yong},
  title     = {Shedding Light on the Underexplored: Tackling the Minor Sign Language Research Topics},
  pages     = {147--158},
  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/24028.html},
  abstract  = {In the past decade, sign language research has achieved remarkable results alongside the advancements in deep learning. However, there is a disconnect between the outcomes of these research efforts and the actual use of sign language by signers. In this position paper, we reviewed sign language papers related to deep learning published in the last ten years to explore reasons for this gap. We found many areas of research that are still underdeveloped, despite their linguistic importance. Based on an analysis of known corpora and methodologies, we identified the reasons for the lack of progress in these areas and propose directions for future research efforts.}
}

@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.}
}