Paper

Abstract Meaning Representation for Human-Robot Dialogue

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Abstract

In this research, we begin to tackle the challenge of natural language understanding (NLU) in the context of the development of a robot dialogue system. We explore the adequacy of Abstract Meaning Representation (AMR) as a conduit for NLU. First, we consider the feasibility of using existing AMR parsers for automatically creating meaning representations for robot-directed transcribed speech data. We evaluate the quality of output of two parsers on this data against a manually annotated gold-standard data set. Second, we evaluate the semantic coverage and distinctions made in AMR overall: how well does it capture the meaning and distinctions needed in our collaborative human-robot dialogue domain? We find that AMR has gaps that align with linguistic information critical for effective human-robot collaboration in search and navigation tasks, and we present task-specific modifications to AMR to address the deficiencies.

Keywords: Dialogue systems, semantics, Natural Language Understanding

How to Cite:

Bonial, C. N., Donatelli, L., Ervin, J. & Voss, C. R., (2019) “Abstract Meaning Representation for Human-Robot Dialogue”, Society for Computation in Linguistics 2(1), 236-246. doi: https://doi.org/10.7275/v3c5-yd35