Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22
This work addresses sign language translation for low-resource settings, but it is incremental as it builds on existing methods with modest gains.
The paper tackled low-resourced sign language translation by developing a Transformer-based system for the WMT-SLT 22 task, achieving a BLEU score of 0.50 on the test set, which improved the baseline by 0.38 BLEU.
This paper describes the system developed at the Universitat Politècnica 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.