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1
Debiasing Methods in Natural Language Understanding Make Bias More Accessible ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.116/ Abstract: Model robustness to bias is often determined by the generalization on carefully designed out-of-distribution datasets. Recent debiasing methods in natural language understanding (NLU) improve performance on such datasets by pressuring models into making unbiased predictions. An underlying assumption behind such methods is that this also leads to the discovery of more robust features in the model’s inner representations. We propose a general probing-based framework that allows for post-hoc interpretation of biases in language models, and use an information-theoretic approach to measure the extractability of certain biases from the model's representations. We experiment with several NLU datasets and known biases, and show that, counter-intuitively, the more a language model is pushed towards a debiased regime, the more bias is actually encoded in its inner representations. ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Natural Language Understanding
URL: https://underline.io/lecture/37517-debiasing-methods-in-natural-language-understanding-make-bias-more-accessible
https://dx.doi.org/10.48448/scbv-tb28
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2
Quintilian and the pedagogy of argument
In: Argumentation. - Heidelberg [u.a.] : Springer Science+Business Media 15 (2001) 3, 277-293
BLLDB
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3
Quintilian and the Pedagogy of Argument
In: Argumentation. - Heidelberg [u.a.] : Springer Science+Business Media 15 (2001) 3, 277-294
OLC Linguistik
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4
The rhetoric of embodiment
In: Rhetoric Society of America. Rhetoric Society quarterly. - London : Taylor & Francis 28 (1998) 4, 29-50
BLLDB
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