Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction
Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten, Patrick Gallinari
Abstract
State-of-the-art NLP models can adopt shallow heuristics that limit their generalization capability (McCoy et al., 2019). Such heuristics include lexical overlap with the training set in Named-Entity Recognition (Taille et al., 2020) and Event or Type heuristics in Relation Extraction (Rosenman et al., 2020). In the more realistic end-to-end RE setting, we can expect yet another heuristic: the mere retention of training relation triples. In this paper we propose two experiments confirming that retention of known facts is a key factor of performance on standard benchmarks. Furthermore, one experiment suggests that a pipeline model able to use intermediate type representations is less prone to over-rely on retention.
BibTeX
@inproceedings{taille-etal-2021-separating,
title = "Separating Retention from Extraction in the Evaluation of End-to-end {R}elation {E}xtraction",
author = "Taill{\'e}, Bruno and
Guigue, Vincent and
Scoutheeten, Geoffrey and
Gallinari, Patrick",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.816/",
doi = "10.18653/v1/2021.emnlp-main.816",
pages = "10438--10449"
}