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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/P19-2014.pdf
- Data
- CommonsenseQA, MCScript, SQuAD