Many downstream NLP tasks have shown significant improvement through continual pre-training, transfer learning and multi-task learning. State-of-the-art approaches in Word Sense Disambiguation today benefit from some of these approaches in conjunction with information sources such as semantic relationships and gloss definitions contained within WordNet. Our work builds upon these systems and uses data augmentation along with extensive pre-training on various different NLP tasks and datasets. Our transfer learning and augmentation pipeline achieves state-of-the-art single model performance in WSD and is at par with the best ensemble results.

翻译：许多下游国家劳工政策任务通过持续的培训前准备、转让学习和多任务学习取得了显著的改进,Word Sense Dismendation中最先进的方法如今与WordNet中包含的语义关系和光谱定义等信息来源一起受益于其中一些方法,我们的工作以这些系统为基础,利用数据扩充,同时对各种国家劳工政策任务和数据集进行广泛的预先培训。我们的转让学习和增强管道在WSD中取得了最先进的单一模型性能,与最佳共同结果相同。