Locke’s Holiday: Belief Bias in Machine Reading

Anders Søgaard


Abstract
I highlight a simple failure mode of state-of-the-art machine reading systems: when contexts do not align with commonly shared beliefs. For example, machine reading systems fail to answer What did Elizabeth want? correctly in the context of ‘My kingdom for a cough drop, cried Queen Elizabeth.’ Biased by co-occurrence statistics in the training data of pretrained language models, systems predict my kingdom, rather than a cough drop. I argue such biases are analogous to human belief biases and present a carefully designed challenge dataset for English machine reading, called Auto-Locke, to quantify such effects. Evaluations of machine reading systems on Auto-Locke show the pervasiveness of belief bias in machine reading.
Anthology ID:
2021.emnlp-main.649
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8240–8245
Language:
URL:
https://aclanthology.org/2021.emnlp-main.649
DOI:
10.18653/v1/2021.emnlp-main.649
Bibkey:
Cite (ACL):
Anders Søgaard. 2021. Locke’s Holiday: Belief Bias in Machine Reading. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8240–8245, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Locke’s Holiday: Belief Bias in Machine Reading (Søgaard, EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.649.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.649.mp4
Data
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