  Condescending language use is caustic; it can bring dialogues to an end and
bifurcate communities. Thus, systems for condescension detection could have a
large positive impact. A challenge here is that condescension is often
impossible to detect from isolated utterances, as it depends on the discourse
and social context. To address this, we present TalkDown, a new labeled dataset
of condescending linguistic acts in context. We show that extending a
language-only model with representations of the discourse improves performance,
and we motivate techniques for dealing with the low rates of condescension
overall. We also use our model to estimate condescension rates in various
online communities and relate these differences to differing community norms.
