@inproceedings{huang-etal-2019-geosqa,
title = "{G}eo{SQA}: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level",
author = "Huang, Zixian and
Shen, Yulin and
Li, Xiao and
Wei, Yu{'}ang and
Cheng, Gong and
Zhou, Lin and
Dai, Xinyu and
Qu, Yuzhong",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1597",
doi = "10.18653/v1/D19-1597",
pages = "5866--5871",
abstract = "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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%0 Conference Proceedings
%T GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level
%A Huang, Zixian
%A Shen, Yulin
%A Li, Xiao
%A Wei, Yu’ang
%A Cheng, Gong
%A Zhou, Lin
%A Dai, Xinyu
%A Qu, Yuzhong
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F huang-etal-2019-geosqa
%X 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.
%R 10.18653/v1/D19-1597
%U https://aclanthology.org/D19-1597
%U https://doi.org/10.18653/v1/D19-1597
%P 5866-5871
Markdown (Informal)
[GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level](https://aclanthology.org/D19-1597) (Huang et al., EMNLP-IJCNLP 2019)
ACL
- Zixian Huang, Yulin Shen, Xiao Li, Yu’ang Wei, Gong Cheng, Lin Zhou, Xinyu Dai, and Yuzhong Qu. 2019. GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5866–5871, Hong Kong, China. Association for Computational Linguistics.