GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark
This addresses the need for standardized evaluation in geographic NLP, which is incremental as it introduces a new benchmark rather than a novel method.
The authors tackled the lack of a unified benchmark for geographic natural language processing by proposing GeoGLUE, a benchmark with six tasks, and demonstrated its effectiveness through evaluation experiments.
With a fast developing pace of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information. However, few researchers focus on geographic natural language processing, and there has never been a benchmark to build a unified standard. In this work, we propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks, including geographic textual similarity on recall, geographic textual similarity on rerank, geographic elements tagging, geographic composition analysis, geographic where what cut, and geographic entity alignment. We also pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.