Affective Natural Language Generation of Event Descriptions through
  Fine-grained Appraisal Conditions
Yarik Menchaca Resendiz Roman Klinger
Models for affective text generation have shown a remarkable progress, but
they commonly rely only on basic emotion theories or valance/arousal values as
conditions. This is appropriate when the goal is to create explicit emotion
statements ("The kid is happy."). Emotions are, however, commonly communicated
implicitly. For instance, the emotional interpretation of an event ("Their dog
died.") does often not require an explicit emotion statement. In psychology,
appraisal theories explain the link between a cognitive evaluation of an event
and the potentially developed emotion. They put the assessment of the situation
on the spot, for instance regarding the own control or the responsibility for
what happens. We hypothesize and subsequently show that including appraisal
variables as conditions in a generation framework comes with two advantages.
(1) The generation model is informed in greater detail about what makes a
specific emotion and what properties it has. This leads to text generation that
better fulfills the condition. (2) The variables of appraisal allow a user to
perform a more fine-grained control of the generated text, by stating
properties of a situation instead of only providing the emotion category. Our
Bart and T5-based experiments with 7 emotions (Anger, Disgust, Fear, Guilt,
Joy, Sadness, Shame), and 7 appraisals (Attention, Responsibility, Control,
Circumstance, Pleasantness, Effort, Certainty) show that (1) adding appraisals
during training improves the accurateness of the generated texts by 10 pp in
F1. Further, (2) the texts with appraisal variables are longer and contain more
details. This exemplifies the greater control for users.