Changing the World by Changing the Data

Anna Rogers


Abstract
NLP community is currently investing a lot more research and resources into development of deep learning models than training data. While we have made a lot of progress, it is now clear that our models learn all kinds of spurious patterns, social biases, and annotation artifacts. Algorithmic solutions have so far had limited success. An alternative that is being actively discussed is more careful design of datasets so as to deliver specific signals. This position paper maps out the arguments for and against data curation, and argues that fundamentally the point is moot: curation already is and will be happening, and it is changing the world. The question is only how much thought we want to invest into that process.
Anthology ID:
2021.acl-long.170
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2182–2194
Language:
URL:
https://aclanthology.org/2021.acl-long.170
DOI:
10.18653/v1/2021.acl-long.170
Bibkey:
Cite (ACL):
Anna Rogers. 2021. Changing the World by Changing the Data. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2182–2194, Online. Association for Computational Linguistics.
Cite (Informal):
Changing the World by Changing the Data (Rogers, ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/remove-xml-comments/2021.acl-long.170.pdf
Video:
 https://preview.aclanthology.org/remove-xml-comments/2021.acl-long.170.mp4