Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22

التفاصيل البيبلوغرافية
العنوان: Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22
المؤلفون: Tarrés, Laia, Gàllego, Gerard I., Giró-i-Nieto, Xavier, Torres, Jordi
سنة النشر: 2022
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition
الوصف: This paper describes the system developed at the Universitat Polit\`ecnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the vocabulary size, data augmentation techniques and pretraining the model with the PHOENIX-14T dataset. Our system obtains 0.50 BLEU score for the test set, improving the organizers' baseline by 0.38 BLEU. We remark the poor results for both the baseline and our system, and thus, the unreliability of our findings.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2212.01140
رقم الأكسشن: edsarx.2212.01140
قاعدة البيانات: arXiv