In this work, we explore the novel idea of employing dependency parsing information in the context of few-shot learning, the task of learning the meaning of a rare word based on a limited amount of context sentences. Firstly, we use dependency-based word embedding models as background spaces for few-shot learning. Secondly, we introduce two few-shot learning methods which enhance the additive baseline model by using dependencies.

翻译：在这项工作中,我们探索了在短小学习中使用依赖分析信息的新理念,即根据有限的上下文句子学习稀有词的含义。首先,我们使用基于依赖的词嵌入模型作为短小学习的背景空间。第二,我们引入了两种通过使用依赖性来强化添加基线模型的微小学习方法。