  Creating agents that can both appropriately respond to conversations and
understand complex human linguistic tendencies and social cues has been a long
standing challenge in the NLP community. A recent pillar of research revolves
around emotion recognition in conversation (ERC); a sub-field of emotion
recognition that focuses on conversations or dialogues that contain two or more
utterances. In this work, we explore an approach to ERC that exploits the use
of neural embeddings along with complex structures in dialogues. We implement
our approach in a framework called Probabilistic Soft Logic (PSL), a
declarative templating language that uses first-order like logical rules, that
when combined with data, define a particular class of graphical model.
Additionally, PSL provides functionality for the incorporation of results from
neural models into PSL models. This allows our model to take advantage of
advanced neural methods, such as sentence embeddings, and logical reasoning
over the structure of a dialogue. We compare our method with state-of-the-art
purely neural ERC systems, and see almost a 20% improvement. With these
results, we provide an extensive qualitative and quantitative analysis over the
DailyDialog conversation dataset.
