Paper Info
Title
Modeling Human Motives and Emotions from Personal Narratives Using External Knowledge And Entity Tracking
Abstract
ABSTRACT The ability to automatically understand and infer characters’ motivations and emotional states is key to better narrative comprehension. In this work, we propose a Transformer-based architecture, referred to as , to model characters’ motives and emotions from personal narratives. Towards this goal, we incorporate social commonsense knowledge about the 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 dataset containing annotations for character mental states. Further, we show that the learned mental state embeddings can be applied in downstream tasks such as empathetic response generation.
Year
DOI
Venue
2021
10.1145/3442381.3449997
International World Wide Web Conference
Keywords
DocType
Citations 
natural language generation, narrative comprehension, representation learning, pragmatics, memory network, mental state representation, entity tracking, social events, social commonsense Knowledge, external memory
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Prashanth Vijayaraghavan1476.20
Deb Roy2103392.10