Using discourse connectives to test genre bias in masked language models

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This paper presents evidence for an effect of genre on the use of discourse connectives in argumentation. Drawing from discourse processing research on reasoning-based structures, we use fill-mask computation to measure genre-induced expectations of argument realisation, and beta regression to model the probabilities of these realisations against a set of predictors. Contrasting fill-mask probabilities for the presence or absence of a discourse connective in baseline and finetuned language models reveals that genre introduces biases for the realisation of argument structure. These outcomes suggest that cross-domain discourse processing, but also argument mining, should take into account generalisations about specific features, such as connectives, and their probability related to the genre context.

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