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.

通过机器标注的社交定位标签，我们展示了社交定位标签如何改善对话结果预测任务的性能，特别是在资源匮乏的环境下，并且在英语和中文语言基准测试中展示了其对话结果预测任务的实用性。