The ability to automatically understand and infer characters’ motivations and their emotional states is key towards better narrative comprehension. In this work, we propose a Transformer-based architecture to model characters’ motives and emotions from personal narratives. Towards this goal, we incorporate social commonsense knowledge about mental states of people related to social events and employ dynamic state tracking of entities using an augmented memory module. Our model learns to produce contextual embeddings and explanations of characters’ mental states by integrating external knowledge along with prior narrative context and mental state encodings. We leverage weakly- annotated personal narratives and knowledge data to train our model and demonstrate its effectiveness on publicly available STORYCOMMONSENSE dataset containing annotations for character mental states. Further, we show that the learnt mental state embeddings can be applied in downstream tasks such as empathetic response generation.
