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Abstract Meaning Representation for Human-Robot Dialogue
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