Semantic Role Labeling (SRL) captures semantic roles (or participants) such as agent, patient, and theme associated with verbs from the text. While it provides important intermediate semantic representations for many traditional NLP tasks (such as information extraction and question answering), it does not capture grounded semantics so that an artificial agent can reason, learn, and perform the actions with respect to the physical environment. To address this problem, this paper extends traditional SRL to grounded SRL where arguments of verbs are grounded to participants of actions in the physical world. By integrating language and vision processing through joint inference, our approach not only grounds explicit roles, but also grounds implicit roles that are not explicitly mentioned in language descriptions. This paper describes our empirical results and discusses challenges and future directions.
展开▼
