Social Orientation: A New Feature for Dialogue Analysis
This work addresses the need for better dialogue analysis tools in conversational AI and psychology, though it is incremental in applying existing psychological theory to machine learning tasks.
The paper tackles the problem of predicting and explaining dialogue outcomes by introducing a new dataset of dialogue utterances machine-labeled with social orientation tags, showing that these tags improve task performance, especially in low-resource settings, on English and Chinese benchmarks.
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.