论文标题
解决低资源的手语翻译：WMT-SLT 22的UPC
Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22
论文作者
论文摘要
本文介绍了在Catalunya大学开发的系统，该系统针对机器翻译的讲习班2022指示语言翻译任务，尤其是在符号到文本方向。我们使用使用Fairseq建模工具包实现的变压器模型。我们已经尝试了使用Phoenix-14T数据集进行词汇大小，数据增强技术和预测模型的实验。我们的系统为测试集获得了0.50 BLEU得分，将组织者的基线提高了0.38 BLEU。我们指出了基线和系统的不良结果，因此我们发现的不可靠性。
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.
