2019 EMNLP EMNLP 2019

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

Abstract

AbstractScenario-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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Interdisciplinary and Natural Language Processing
📈 Trend Setter — Question Answering
🧭 Keyword Pioneer — multi-source knowledge
🐣 Hot Topic Early Bird — knowledge retrieval
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio