ABSTRACT
We recently introduced DRaiL, a declarative neuro-symbolic modeling framework designed to support a wide variety of NLP scenarios. In this demo, we enhance DRaiL with an easy to use Python interface equipped with methods to define, modify and augment models interactively, as well as with methods to debug and visualize the predictions made. We demonstrate this interface with two challenging NLP tasks: analyzing moral sentiment in political discourse, and analyzing opinions about the Covid-19 vaccine. © 2022 Association for Computational Linguistics.
ABSTRACT
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and fine-grained entity level moral sentiment analysis. We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predictions even in the low-supervision settings.