Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach
  for Relation Classification
Robert Vacareanu Fahmida Alam Md Asiful Islam Haris Riaz Mihai Surdeanu
This paper introduces a novel neuro-symbolic architecture for relation
classification (RC) that combines rule-based methods with contemporary deep
learning techniques. This approach capitalizes on the strengths of both
paradigms: the adaptability of rule-based systems and the generalization power
of neural networks. Our architecture consists of two components: a declarative
rule-based model for transparent classification and a neural component to
enhance rule generalizability through semantic text matching. Notably, our
semantic matcher is trained in an unsupervised domain-agnostic way, solely with
synthetic data. Further, these components are loosely coupled, allowing for
rule modifications without retraining the semantic matcher. In our evaluation,
we focused on two few-shot relation classification datasets: Few-Shot TACRED
and a Few-Shot version of NYT29. We show that our proposed method outperforms
previous state-of-the-art models in three out of four settings, despite not
seeing any human-annotated training data. Further, we show that our approach
remains modular and pliable, i.e., the corresponding rules can be locally
modified to improve the overall model. Human interventions to the rules for the
TACRED relation \texttt{org:parents} boost the performance on that relation by
as much as 26\% relative improvement, without negatively impacting the other
relations, and without retraining the semantic matching component.