Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictions on rare word senses due to limited training data and can only select the best definition sentence from one predefined word sense inventory (e.g., WordNet). To address the data sparsity problem and generalize the model to be independent of one predefined inventory, we propose a gloss alignment algorithm that can align definition sentences (glosses) with the same meaning from different sense inventories to collect rich lexical knowledge. We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks. Experiments on benchmark datasets show that the proposed method improves predictions on both frequent and rare word senses, outperforming prior work by 1.2% on the All-Words WSD Task and 4.3% on the Low-Shot WSD Task. Evaluation on WiC Task also indicates that our method can better capture word meanings in context.

翻译：Wordsense Disandguation (WSD) 旨在根据一个词的背景自动识别一个词的确切含义。 由于培训数据有限, 现有的受监督模型试图对稀有字感做出准确的预测, 但由于培训数据有限, 并且只能从一个预定义的字感清清册中选择最佳定义句( 如 WordNet ) 。 为了解决数据宽度问题, 并推广模型, 使之独立于一个预定义的清册, 我们提议了一个 Gloss 校正算法, 它可以将定义句( grosseseses) 与从不同意义的清册中收集丰富的词汇知识的相同含义相匹配。 然后, 我们用这些一致的清册来训练一个模型, 以辨别一个目标字在上下文中和其中的一个符号之间的语义等同性。 对基准数据集的实验表明, 拟议的方法可以改进对常见和稀有字觉的词学的预测, 比全部Words WSD 任务1.2% 和低点 WSD任务4.3% 的先前工作要好。 对 WIC 任务的评估还表明, 我们的方法可以更好地捕捉到背景中的字义含义。