@inproceedings{kim-etal-2022-layering:lrec,
  author    = {Kim, Jung-Ho and Hwang, Eui Jun and Cho, Sukmin and Lee, Du Hui and Park, Jong C.},
  title     = {Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words},
  pages     = {1519--1528},
  editor    = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric 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 Odijk, Jan and Piperidis, Stelios},
  booktitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {20--25},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-72-6},
  language  = {english},
  url       = {https://aclanthology.org/2022.lrec-1.163},
  abstract  = {Sign language production (SLP) is the process of generating sign language videos from spoken language expressions. Since sign languages are highly under-resourced, existing vision-based SLP approaches suffer from out-of-vocabulary (OOV) and test-time generalization problems and thus generate low-quality translations. To address these problems, we introduce an avatar-based SLP system composed of a sign language translation (SLT) model and an avatar animation generation module. Our Transformer-based SLT model utilizes two additional strategies to resolve these problems: named entity transformation to reduce OOV tokens and context vector generation using a pretrained language model (e.g., BERT) to reliably train the decoder. Our system is validated on a new Korean-Korean Sign Language (KSL) dataset of weather forecasts and emergency announcements. Our SLT model achieves an 8.77 higher BLEU-4 score and a 4.57 higher ROUGE-L score over those of our baseline model. In a user evaluation, 93.48{\%} of named entities were successfully identified by participants, demonstrating marked improvement on OOV issues.}
}