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.
翻译:本文介绍加泰罗尼亚Politáñçéécnica大学为2022年手语手语翻译工作讲习班开发的系统,特别是手对文本方向的系统,我们使用与Fairseq模型工具包一起实施的变换模型,我们试验了词汇大小、数据增强技术,并用PHOENIX-14T数据集对模型进行了预先培训,我们的系统为测试集获得了0.50 BLEU分,将组织者的基线提高了0.38 BLEU。我们指出,基线和我们的系统的结果都很差,因此,我们的调查结果是不可靠的。