  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.
