  The notion of face described by Brown and Levinson (1987) has been studied in
great detail, but a critical aspect of the framework, that which focuses on how
intentions mediate the planning of turns which impose upon face, has received
far less attention. We present an analysis of three computational systems
trained for classifying both intention and politeness, focusing on how the
former influences the latter. In politeness theory, agents attend to the desire
to have their wants appreciated (positive face), and a complementary desire to
act unimpeded and maintain freedom (negative face). Similar to speech acts,
utterances can perform so-called face acts which can either raise or threaten
the positive or negative face of the speaker or hearer. We begin by using an
existing corpus to train a model which classifies face acts, achieving a new
SoTA in the process. We then observe that every face act has an underlying
intention that motivates it and perform additional experiments integrating
dialog act annotations to provide these intentions by proxy. Our analysis finds
that dialog acts improve performance on face act detection for minority classes
and points to a close relationship between aspects of face and intent.
