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.
Anthology ID:
2021.emnlp-main.816
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10438–10449
Language:
URL:
https://aclanthology.org/2021.emnlp-main.816
DOI:
10.18653/v1/2021.emnlp-main.816
Bibkey:
Cite (ACL):
Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten, and Patrick Gallinari. 2021. Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10438–10449, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction (Taillé et al., EMNLP 2021)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/2021.emnlp-main.816.pdf
Video:
 https://preview.aclanthology.org/nschneid-patch-2/2021.emnlp-main.816.mp4
Data
SciERC