Textual Inference and Meaning Representation in Human Robot Interaction

This paper provides a first investigation over existing textual inference paradigms in order to propose a generic framework able to capture major semantic aspects in Human Robot Interaction (HRI). We investigate the use of general semantic paradigms used in Natural Language Understanding (NLU) tasks, such as Semantic Role Labeling, over typical robot commands. The semantic information obtained is then represented under the Abstract Meaning Representation. AMR is a general representation language useful to express different level of semantic information without a strong dependence to the syntactic structure of an underlying sentence. The final aim of this work is to find an effective synergy between HRI and NLU.