论文标题
用光泽选择目标和示例句子适应单词感官歧义的歧义
Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences
论文作者
论文摘要
使用预训练的语言模型（例如BERT），域的适应或转移学习已被证明是许多自然语言处理任务的有效方法。在这项工作中，我们建议将单词意义上的歧义作为相关排名任务，并在序列对排名任务上进行微调BERT，以选择给定上下文句子和候选人感觉定义列表的最可能的理智定义。我们还使用WordNet的现有示例句子为WSD介绍了一项数据增强技术。使用拟议的培训目标和数据增强技术，我们的模型能够在英语全字基准数据集上实现最先进的结果。
Domain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks. In this work, we propose to formulate word sense disambiguation as a relevance ranking task, and fine-tune BERT on sequence-pair ranking task to select the most probable sense definition given a context sentence and a list of candidate sense definitions. We also introduce a data augmentation technique for WSD using existing example sentences from WordNet. Using the proposed training objective and data augmentation technique, our models are able to achieve state-of-the-art results on the English all-words benchmark datasets.
