Yiwei Luo


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Detecting Stance in Media On Global Warming
Yiwei Luo | Dallas Card | Dan Jurafsky
Findings of the Association for Computational Linguistics: EMNLP 2020

Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, “Leading scientists agree that global warming is a serious concern,” framing a clause which affirms their own stance (“that global warming is serious”) as an opinion endorsed ("[scientists] agree”) by a reputable source (“leading”). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: “Mistaken scientists claim [...]." Our work studies opinion-framing in the global warming (GW) debate, an increasingly partisan issue that has received little attention in NLP. We introduce DeSMOG, a dataset of stance-labeled GW sentences, and train a BERT classifier to study novel aspects of argumentation in how different sides of a debate represent their own and each other’s opinions. From 56K news articles, we find that similar linguistic devices for self-affirming and opponent-doubting discourse are used across GW-accepting and skeptic media, though GW-skeptical media shows more opponent-doubt. We also find that authors often characterize sources as hypocritical, by ascribing opinions expressing the author’s own view to source entities known to publicly endorse the opposing view. We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.


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From Insanely Jealous to Insanely Delicious: Computational Models for the Semantic Bleaching of English Intensifiers
Yiwei Luo | Dan Jurafsky | Beth Levin
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

We introduce novel computational models for modeling semantic bleaching, a widespread category of change in which words become more abstract or lose elements of meaning, like the development of “arrive” from its earlier meaning ‘become at shore.’ We validate our methods on a widespread case of bleaching in English: de-adjectival adverbs that originate as manner adverbs (as in “awfully behaved”) and later become intensifying adverbs (as in “awfully nice”). Our methods formally quantify three reflexes of bleaching: decreasing similarity to the source meaning (e.g., “awful”), increasing similarity to a fully bleached prototype (e.g., “very”), and increasing productivity (e.g., the breadth of adjectives that an adverb modifies). We also test a new causal model and find evidence that bleaching is initially triggered in contexts such as “conspicuously evident” and “insanely jealous”, where an adverb premodifies a semantically similar adjective. These contexts provide a form of “bridging context” (Evans and Wilkins, 2000) that allow a manner adverb to be reinterpreted as an intensifying adverb similar to “very”.