It is an established assumption that pattern-based models are good at precision, while learning based models are better at recall. But is that really the case? I argue that there are two kinds of recall: d-recall, reflecting diversity, and e-recall, reflecting exhaustiveness. I demonstrate through experiments that while neural methods are indeed significantly better at d-recall, it is sometimes the case that pattern-based methods are still substantially better at e-recall. Ideal methods should aim for both kinds, and this ideal should in turn be reflected in our evaluations.
Cite
@article{arxiv.2303.10527,
title = {Two Kinds of Recall},
author = {Yoav Goldberg},
journal= {arXiv preprint arXiv:2303.10527},
year = {2023}
}