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GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level

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arxiv 1908.07855 v1 pith:44XVIYFA submitted 2019-08-20 cs.CL

classification cs.CL
keywords answeringgeographyquestionresearchbenchmarkdomaingeosqahigh
verification ladder T0 review T1 audit T2 compute T3 formal
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Scenario-based question answering (SQA) has attracted increasing research attention. It typically requires retrieving and integrating knowledge from multiple sources, and applying general knowledge to a specific case described by a scenario. SQA widely exists in the medical, geography, and legal domains---both in practice and in the exams. In this paper, we introduce the GeoSQA dataset. It consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level, where diagrams (e.g., maps, charts) have been manually annotated with natural language descriptions to benefit NLP research. Benchmark results on a variety of state-of-the-art methods for question answering, textual entailment, and reading comprehension demonstrate the unique challenges presented by SQA for future research.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture

    cs.AI 2024-12 reject novelty 3.0 of 10

    Text2Insight combines an LLM text-to-SQL step with a rule-based chart predictor and BERT-based question answering and prediction, but its end-to-end performance claims rest on circular or missing evaluation.

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