Building and Learning Structures in a Situated Blocks World Through Deep Language Understanding

Ian Perera, James Allen, Choh Man Teng, Lucian Galescu


Abstract
We demonstrate a system for understanding natural language utterances for structure description and placement in a situated blocks world context. By relying on a rich, domain-specific adaptation of a generic ontology and a logical form structure produced by a semantic parser, we obviate the need for an intermediate, domain-specific representation and can produce a reasoner that grounds and reasons over concepts and constraints with real-valued data. This linguistic base enables more flexibility in interpreting natural language expressions invoking intrinsic concepts and features of structures and space. We demonstrate some of the capabilities of a system grounded in deep language understanding and present initial results in a structure learning task.
Anthology ID:
W18-1402
Volume:
Proceedings of the First International Workshop on Spatial Language Understanding
Month:
June
Year:
2018
Address:
New Orleans
Venues:
NAACL | SpLU | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–20
Language:
URL:
https://www.aclweb.org/anthology/W18-1402
DOI:
10.18653/v1/W18-1402
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-1402.pdf