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

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 (Taillé et al., 2020a) 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.