We present a novel method for using agent experiences gathered through an embodied simulation to ground contextualized word vectors to object representations. We use similarity learning to make comparisons between different object types based on their properties when interacted with, and to extract common features pertaining to the objects' behavior. We then use an affine transformation to calculate a projection matrix that transforms contextualized word vectors from different transformer-based language models into this learned space, and evaluate whether new test instances of transformed token vectors identify the correct concept in the object embedding space. Our results expose properties of the embedding spaces of four different transformer models and show that grounding object token vectors is usually more helpful to grounding verb and attribute token vectors than the reverse, which reflects earlier conclusions in the analogical reasoning and psycholinguistic literature.

我们提出了一种新方法，利用通过具身模拟收集的代理经验来将情境化词向量与对象表示相关联，并使用相似性学习来比较不同对象类型之间的相似性，显示出不同转换器模型的嵌入空间的特性，以及用于嵌入对象令牌向量比用于嵌入动词和属性令牌向量更有帮助。

在具身模拟中通过相似性学习基于概念的词汇的内涵和区分