Abstract

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Commonsense knowledge about the typical functions of physical objects allows people to make inferences during sentence understanding. For example, we infer that "Sam enjoyed the book" means that Sam enjoyed reading the book, even though the action is implicit. Prior research has focused on learning the prototypical functions of physical objects in order to enable inferences about implicit actions. But many sentences refer to objects even when they are not used (e.g., "The book fell" ). We argue that NLP systems need to recognize whether an object is being used before inferring how the object is used. We define a new task called Object Use Classification that determines whether a physical object mentioned in a sentence was used or likely will be used. We present a new dataset and a classification model for this task that exploits data augmentation methods and FrameNet data when fine-tuning a pre-trained language model. We also show that object use classification combined with knowledge about the prototypical functions of objects has the potential to yield very good inferences about implicit and anticipated actions.

Downloads

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