  There are many settings where it is useful to predict and explain the success
or failure of a dialogue. Circumplex theory from psychology models the social
orientations (e.g., Warm-Agreeable, Arrogant-Calculating) of conversation
participants and can be used to predict and explain the outcome of social
interactions. Our work is novel in its systematic application of social
orientation tags to modeling conversation outcomes. In this paper, we introduce
a new data set of dialogue utterances machine-labeled with social orientation
tags. We show that social orientation tags improve task performance, especially
in low-resource settings, on both English and Chinese language benchmarks. We
also demonstrate how social orientation tags help explain the outcomes of
social interactions when used in neural models. Based on these results showing
the utility of social orientation tags for dialogue outcome prediction tasks,
we release our data sets, code, and models that are fine-tuned to predict
social orientation tags on dialogue utterances.
