Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy

Diana Galván-Sosa


Abstract
Reading comprehension (RC) through question answering is a useful method for evaluating if a reader understands a text. Standard accuracy metrics are used for evaluation, where high accuracy is taken as indicative of a good understanding. However, literature in quality learning suggests that task performance should also be evaluated on the undergone process to answer. The Question-Answer Relationship (QAR) is one of the strategies for evaluating a reader’s understanding based on their ability to select different sources of information depending on the question type. We propose the creation of a dataset to learn the QAR strategy with weak supervision. We expect to complement current work on reading comprehension by introducing a new setup for evaluation.
Anthology ID:
P19-2014
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Fernando Alva-Manchego, Eunsol Choi, Daniel Khashabi
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
106–112
Language:
URL:
https://aclanthology.org/P19-2014
DOI:
10.18653/v1/P19-2014
Bibkey:
Cite (ACL):
Diana Galván-Sosa. 2019. Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 106–112, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy (Galván-Sosa, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-2014.pdf
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