ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning

Michael Boratko, Xiang Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum


Abstract
Given questions regarding some prototypical situation — such as Name something that people usually do before they leave the house for work? — a human can easily answer them via acquired experiences. There can be multiple right answers for such questions, with some more common for a situation than others. This paper introduces a new question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations. The training set is gathered from an existing set of questions played in a long-running international trivia game show – Family Feud. The hidden evaluation set is created by gathering answers for each question from 100 crowd-workers. We also propose a generative evaluation task where a model has to output a ranked list of answers, ideally covering all prototypical answers for a question. After presenting multiple competitive baseline models, we find that human performance still exceeds model scores on all evaluation metrics with a meaningful gap, supporting the challenging nature of the task.
Anthology ID:
2020.emnlp-main.85
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1122–1136
Language:
URL:
https://aclanthology.org/2020.emnlp-main.85
DOI:
10.18653/v1/2020.emnlp-main.85
Bibkey:
Cite (ACL):
Michael Boratko, Xiang Li, Tim O’Gorman, Rajarshi Das, Dan Le, and Andrew McCallum. 2020. ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1122–1136, Online. Association for Computational Linguistics.
Cite (Informal):
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning (Boratko et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.85.pdf
Optional supplementary material:
 2020.emnlp-main.85.OptionalSupplementaryMaterial.zip
Video:
 https://slideslive.com/38939102
Code
 iesl/protoqa-data
Data
ProtoQAConceptNet