  Qualitative causal relationships compactly express the direction, dependency,
temporal constraints, and monotonicity constraints of discrete or continuous
interactions in the world. In everyday or academic language, we may express
interactions between quantities (e.g., sleep decreases stress), between
discrete events or entities (e.g., a protein inhibits another protein's
transcription), or between intentional or functional factors (e.g., hospital
patients pray to relieve their pain). This paper presents a transformer-based
NLP architecture that jointly identifies and extracts (1) variables or factors
described in language, (2) qualitative causal relationships over these
variables, and (3) qualifiers and magnitudes that constrain these causal
relationships. We demonstrate this approach and include promising results from
in two use cases, processing textual inputs from academic publications, news
articles, and social media.
