论文标题
神经手语翻译通过学习令牌化
Neural Sign Language Translation by Learning Tokenization
论文作者
论文摘要
手语翻译最近取得了巨大的成功，这提出了改善与聋人交流的希望。一个称为令牌化的预处理步骤改善了翻译的成功。如果有监督的数据，可以从符号视频中学到令牌。但是，光泽级别的数据注释昂贵，注释数据很少。该论文利用对抗性，多任务，转移学习来寻找半监督的令牌化方法，而无需额外的标签负担。它提供了广泛的实验，以比较不同设置中的所有方法以进行更深入的分析。如果没有其他目标注释除了句子，则提出的方法可达到13.25 Blue-4和36.28 Rouge分数，该分数将当前的最新面积提高了4分，而Rouge的蓝色4分和5分。
Sign Language Translation has attained considerable success recently, raising hopes for improved communication with the Deaf. A pre-processing step called tokenization improves the success of translations. Tokens can be learned from sign videos if supervised data is available. However, data annotation at the gloss level is costly, and annotated data is scarce. The paper utilizes Adversarial, Multitask, Transfer Learning to search for semi-supervised tokenization approaches without burden of additional labeling. It provides extensive experiments to compare all the methods in different settings to conduct a deeper analysis. In the case of no additional target annotation besides sentences, the proposed methodology attains 13.25 BLUE-4 and 36.28 ROUGE scores which improves the current state-of-the-art by 4 points in BLUE-4 and 5 points in ROUGE.
