Do Transformer Networks Improve the Discovery of Rules from Text?

no code implementations • LREC 2022 • Mahdi Rahimi, Mihai Surdeanu

With their Discovery of Inference Rules from Text (DIRT) algorithm, Lin and Pantel (2001) made a seminal contribution to the field of rule acquisition from text, by adapting the distributional hypothesis of Harris (1954) to rules that model binary relations such as X treat Y. DIRT’s relevance is renewed in today’s neural era given the recent focus on interpretability in the field of natural language processing.

Language Modelling Question Answering

Improving Zero-shot Relation Classification via Automatically-acquired Entailment Templates

no code implementations • Proceedings of the 8th Workshop on Representation Learning for NLP 2023 • Mahdi Rahimi, Mihai Surdeanu

While fully supervised relation classification (RC) models perform well on large-scale datasets, their performance drops drastically in low-resource settings.

Natural Language Inference Relation +2