Proceedings of the First International Workshop on Spatial Language Understanding

Parisa Kordjamshidi, Archna Bhatia, James Pustejovsky, Marie-Francine Moens (Editors)


Anthology ID:
W18-14
Month:
June
Year:
2018
Address:
New Orleans
Venues:
NAACL | SpLU | WS
SIG:
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/W18-14
DOI:
10.18653/v1/W18-14
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PDF:
https://aclanthology.org/W18-14.pdf

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Proceedings of the First International Workshop on Spatial Language Understanding
Parisa Kordjamshidi | Archna Bhatia | James Pustejovsky | Marie-Francine Moens

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Points, Paths, and Playscapes : Large-scale Spatial Language Understanding Tasks Set in the Real World
Jason Baldridge | Tania Bedrax-Weiss | Daphne Luong | Srini Narayanan | Bo Pang | Fernando Pereira | Radu Soricut | Michael Tseng | Yuan Zhang

Spatial language understanding is important for practical applications and as a building block for better abstract language understanding. Much progress has been made through work on understanding spatial relations and values in images and texts as well as on giving and following navigation instructions in restricted domains. We argue that the next big advances in spatial language understanding can be best supported by creating large-scale datasets that focus on points and paths based in the real world, and then extending these to create online, persistent playscapes that mix human and bot players, where the bot players must learn, evolve, and survive according to their depth of understanding of scenes, navigation, and interactions.

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The Case for Systematically Derived Spatial Language Usage
Bonnie Dorr | Clare Voss

This position paper argues that, while prior work in spatial language understanding for tasks such as robot navigation focuses on mapping natural language into deep conceptual or non-linguistic representations, it is possible to systematically derive regular patterns of spatial language usage from existing lexical-semantic resources. Furthermore, even with access to such resources, effective solutions to many application areas such as robot navigation and narrative generation also require additional knowledge at the syntax-semantics interface to cover the wide range of spatial expressions observed and available to natural language speakers. We ground our insights in, and present our extensions to, an existing lexico-semantic resource, covering 500 semantic classes of verbs, of which 219 fall within a spatial subset. We demonstrate that these extensions enable systematic derivation of regular patterns of spatial language without requiring manual annotation.