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Text-to-OverpassQL: a natural language interface for complex geodata querying of OpenStreetMap

We present Text-to-OverpassQL, a task designed to facilitate a natural language interface for querying geodata from OpenStreetMap (OSM). The Overpass Query Language (OverpassQL) allows users to formulate complex database queries and is widely adopted in the OSM ecosystem. Generating Overpass queries...

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Main Authors: Staniek, Michael (Author) , Schumann, Raphael (Author) , Züfle, Maike (Author) , Riezler, Stefan (Author)
Format: Article (Journal)
Language:English
Published: April 30, 2024
In: Transactions of the Association for Computational Linguistics
Year: 2024, Volume: 12, Pages: 562-575
ISSN:2307-387X
DOI:10.1162/tacl_a_00654
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1162/tacl_a_00654
Verlag, kostenfrei, Volltext: https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00654/120832/Text-to-OverpassQL-A-Natural-Language-Interface
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Author Notes:Michael Staniek, Raphael Schumann, Maike Züfle, Stefan Riezler

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520 |a We present Text-to-OverpassQL, a task designed to facilitate a natural language interface for querying geodata from OpenStreetMap (OSM). The Overpass Query Language (OverpassQL) allows users to formulate complex database queries and is widely adopted in the OSM ecosystem. Generating Overpass queries from natural language input serves multiple use-cases. It enables novice users to utilize OverpassQL without prior knowledge, assists experienced users with crafting advanced queries, and enables tool-augmented large language models to access information stored in the OSM database. In order to assess the performance of current sequence generation models on this task, we propose OverpassNL,1 a dataset of 8,352 queries with corresponding natural language inputs. We further introduce task specific evaluation metrics and ground the evaluation of the Text-to-OverpassQL task by executing the queries against the OSM database. We establish strong baselines by finetuning sequence-to-sequence models and adapting large language models with in-context examples. The detailed evaluation reveals strengths and weaknesses of the considered learning strategies, laying the foundations for further research into the Text-to-OverpassQL task. 
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