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What a neural language model tells us about spatial relations

Conference paper
Authors Mehdi Ghanimifard
Simon Dobnik
Published in Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP) at NAACL-HLT 2019, June 6, 2019 Minneapolis, Minnesota, USA
ISBN 978-1-950737-09-3
Publisher Association of Computational Linguistics (ACL)
Place of publication Minneapolis, Minnesota, USA
Publication year 2019
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links https://doi.org/10.18653/v1/W19-160...
https://gup.ub.gu.se/file/207859
https://gup.ub.gu.se/file/207860
Keywords spatial description, spatial language, neural language model, vector representation, spatial reasoning
Subject categories Cognitive science, Computer Vision and Robotics (Autonomous Systems), Language Technology (Computational Linguistics)

Abstract

Understanding and generating spatial descriptions requires knowledge about what objects are related, their functional interactions, and where the objects are geometrically located. Different spatial relations have different functional and geometric bias. The wide usage of neural language models in different areas including generation of image description motivates the study of what kind of knowledge is encoded in neural language models about individual spatial relations. With the premise that the functional bias of relations is expressed in their word distributions, we construct multi-word distributional vector representations and show that these representations perform well on intrinsic semantic reasoning tasks, thus confirming our premise. A comparison of our vector representations to human semantic judgments indicates that different bias (functional or geometric) is captured in different data collection tasks which suggests that the contribution of the two meaning modalities is dynamic, related to the context of the task.

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