@inproceedings{sogaard-2021-lockes,
title = "Locke{'}s Holiday: Belief Bias in Machine Reading",
author = "S{\o}gaard, Anders",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.649",
doi = "10.18653/v1/2021.emnlp-main.649",
pages = "8240--8245",
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 \textit{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 \textit{my kingdom}, rather than \textit{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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Locke’s Holiday: Belief Bias in Machine Reading
%A Søgaard, Anders
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F sogaard-2021-lockes
%X 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.
%R 10.18653/v1/2021.emnlp-main.649
%U https://aclanthology.org/2021.emnlp-main.649
%U https://doi.org/10.18653/v1/2021.emnlp-main.649
%P 8240-8245
Markdown (Informal)
[Locke’s Holiday: Belief Bias in Machine Reading](https://aclanthology.org/2021.emnlp-main.649) (Søgaard, EMNLP 2021)
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.