论文标题
学会学会消除歧义：几个单词sense的元学习歧义
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
论文作者
论文摘要
深度学习方法的成功取决于注释的大型培训数据集，以实现感兴趣的任务。与人类智能相比，这些方法缺乏多功能性和难以迅速学习和适应新任务的方法，在这些任务很少。 Meta学习旨在通过在许多少数任务上训练模型来解决这个问题，并目的是从少数示例中快速学习新任务。在本文中，我们提出了一个元学习框架，用于几个单词sense dismagation（WSD），其目标是学会从仅少数标记的实例中删除看不见的单词。到目前为止，元学习方法通常在$ n $ Way，$ k $ -shot的分类设置中进行了测试，其中每个任务都有$ n $类，每个班级都有$ k $示例。由于其性质，WSD偏离了该受控设置，并要求模型处理大量高度不平衡的类。我们将几种流行的元学习方法扩展到了这种情况，并在这个新的挑战性环境中分析了它们的优势和劣势。
The success of deep learning methods hinges on the availability of large training datasets annotated for the task of interest. In contrast to human intelligence, these methods lack versatility and struggle to learn and adapt quickly to new tasks, where labeled data is scarce. Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples. In this paper, we propose a meta-learning framework for few-shot word sense disambiguation (WSD), where the goal is to learn to disambiguate unseen words from only a few labeled instances. Meta-learning approaches have so far been typically tested in an $N$-way, $K$-shot classification setting where each task has $N$ classes with $K$ examples per class. Owing to its nature, WSD deviates from this controlled setup and requires the models to handle a large number of highly unbalanced classes. We extend several popular meta-learning approaches to this scenario, and analyze their strengths and weaknesses in this new challenging setting.
