David Beaver


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Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias
Venkata Subrahmanyan Govindarajan | David Beaver | Kyle Mahowald | Junyi Jessy Li
Findings of the Association for Computational Linguistics: ACL 2023

While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior. In this paper, we investigate if two pragmatic features (specificity and affect) systematically vary in different intergroup contexts — thus connecting this new framing of bias to language output. Preliminary analysis finds modest correlations between specificity and affect of tweets with supervised intergroup relationship (IGR) labels. Counterfactual probing further reveals that while neural models finetuned for predicting IGR reliably use affect in classification, the model’s usage of specificity is inconclusive.


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To Memorize or to Predict: Prominence labeling in Conversational Speech
Ani Nenkova | Jason Brenier | Anubha Kothari | Sasha Calhoun | Laura Whitton | David Beaver | Dan Jurafsky
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference