Papers
arxiv:1908.07855

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

Published on Aug 20, 2019
Authors:
,
,
,
,
,
,
,

Abstract

The GeoSQA dataset presents a challenging scenario-based question answering benchmark with 1,981 scenarios and 4,110 multiple-choice questions in geography, incorporating diagram annotations for NLP research.

AI-generated summary

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.

Community

Sign up or log in to comment

Models citing this paper 0

No model linking this paper

Cite arxiv.org/abs/1908.07855 in a model README.md to link it from this page.

Datasets citing this paper 1

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/1908.07855 in a Space README.md to link it from this page.

Collections including this paper 0

No Collection including this paper

Add this paper to a collection to link it from this page.