The WiC task has attracted considerable attention in the NLP community, as demonstrated by the popularity of the recent MCL-WiC SemEval task. WSD systems and lexical resources have been used for the WiC task, as well as for WiC dataset construction. TSV is another task related to both WiC and WSD. We aim to establish the exact relationship between WiC, TSV, and WSD. We demonstrate that these semantic classification problems can be pairwise reduced to each other, and so they are theoretically equivalent. We analyze the existing WiC datasets to validate this equivalence hypothesis. We conclude that our understanding of semantic tasks can be increased through the applications of tools from theoretical computer science. Our findings also suggests that more efficient and simpler methods for one of these tasks could be successfully applied in the other two.
翻译:正如最近MCL-WIC SemEval任务的受欢迎程度所证明的那样, WIC任务在NLP社区引起了相当大的关注,WIC系统和词汇资源被用于WIC任务以及WIC数据集的构建。TSV是WIC和WSD的又一项任务。我们的目标是确定WIC、TSV和WSD之间的确切关系。我们证明这些语义分类问题可以相互对等,因此在理论上是等同的。我们分析了现有的WIC数据集,以证实这一等同假设。我们的结论是,通过应用理论计算机科学的工具,可以提高对语义任务的理解。我们的调查结果还表明,对于其中一项任务,可以成功地在另外两项任务中采用更有效和更简单的方法。