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arXiv · 2109.12008

Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction

Bruno Taillé·Vincent Guigue·Geoffrey Scoutheeten·Patrick Gallinari
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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 (Taill\'e 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 several 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.

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Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten, Patrick Gallinari. 2021-09-24. Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction. https://arxiv.org/abs/2109.12008

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