Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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 (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.

12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics