Brandon Waldon


2021

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Modeling cross-linguistic production of referring expressions
Brandon Waldon | Judith Degen
Proceedings of the Society for Computation in Linguistics 2021

2020

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Modeling Behavior in Truth Value Judgment Task Experiments
Brandon Waldon | Judith Degen
Proceedings of the Society for Computation in Linguistics 2020

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Linguistic interpretation as inference under argument system uncertainty: the case of epistemic must
Brandon Waldon
Proceedings of the Probability and Meaning Conference (PaM 2020)

Modern semantic analyses of epistemic language (incl. the modals must and might) can be characterized by the following ‘credence assumption’: speakers have full certainty regarding the propositions that structure their epistemic state. Intuitively, however: a) speakers have graded, rather than categorical, commitment to these propositions, which are often never fully and explicitly articulated; b) listeners have higher-order uncertainty about this speaker uncertainty; c) must p is used to communicate speaker commitment to some conclusion p and to indicate speaker commitment to the premises that condition the conclusion. I explore the consequences of relaxing the credence assumption by extending the argument system semantic framework first proposed by Stone (1994) to a Bayesian probabilistic framework of modeling pragmatic interpretation (Goodman and Frank, 2016). The analysis makes desirable predictions regarding the behavior and interpretation of must, and it suggests a new way of considering the nature of context and communicative exchange.

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Explaining the Trump Gap in Social Distancing Using COVID Discourse
Austin Van Loon | Sheridan Stewart | Brandon Waldon | Shrinidhi K Lakshmikanth | Ishan Shah | Sharath Chandra Guntuku | Garrick Sherman | James Zou | Johannes Eichstaedt
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities’ response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the “”Trump Gap”, or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaning-making in part determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.