IIRC: A Dataset of Incomplete Information Reading Comprehension Questions

James Ferguson, Matt Gardner, Hannaneh Hajishirzi, Tushar Khot, Pradeep Dasigi


Abstract
Humans often have to read multiple documents to address their information needs. However, most existing reading comprehension (RC) tasks only focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a system’s performance at identifying a potential lack of sufficient information and locating sources for that information. To fill this gap, we present a dataset, IIRC, with more than 13K questions over paragraphs from English Wikipedia that provide only partial information to answer them, with the missing information occurring in one or more linked documents. The questions were written by crowd workers who did not have access to any of the linked documents, leading to questions that have little lexical overlap with the contexts where the answers appear. This process also gave many questions without answers, and those that require discrete reasoning, increasing the difficulty of the task. We follow recent modeling work on various reading comprehension datasets to construct a baseline model for this dataset, finding that it achieves 31.1% F1 on this task, while estimated human performance is 88.4%. The dataset, code for the baseline system, and a leaderboard can be found at https://allennlp.org/iirc.
Anthology ID:
2020.emnlp-main.86
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:
1137–1147
Language:
URL:
https://aclanthology.org/2020.emnlp-main.86
DOI:
10.18653/v1/2020.emnlp-main.86
Bibkey:
Cite (ACL):
James Ferguson, Matt Gardner, Hannaneh Hajishirzi, Tushar Khot, and Pradeep Dasigi. 2020. IIRC: A Dataset of Incomplete Information Reading Comprehension Questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1137–1147, Online. Association for Computational Linguistics.
Cite (Informal):
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions (Ferguson et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.86.pdf
Video:
 https://slideslive.com/38939237
Data
IIRCDROPHotpotQANatural QuestionsNewsQASQuADTyDi QA