CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan Berant


Abstract
When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background. To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering. To capture common sense beyond associations, we extract from ConceptNet (Speer et al., 2017) multiple target concepts that have the same semantic relation to a single source concept. Crowd-workers are asked to author multiple-choice questions that mention the source concept and discriminate in turn between each of the target concepts. This encourages workers to create questions with complex semantics that often require prior knowledge. We create 12,247 questions through this procedure and demonstrate the difficulty of our task with a large number of strong baselines. Our best baseline is based on BERT-large (Devlin et al., 2018) and obtains 56% accuracy, well below human performance, which is 89%.
Anthology ID:
N19-1421
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4149–4158
Language:
URL:
https://aclanthology.org/N19-1421
DOI:
10.18653/v1/N19-1421
Award:
 Best Resource Paper
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/N19-1421.pdf
Video:
 https://vimeo.com/365154751