Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Finding Influential Instances for Distantly Supervised Relation
  Extraction   Distant supervision (DS) is a strong way to expand the datasets for enhancing
relation extraction (RE) models but often suffers from high label noise.
Current works based on attention, reinforcement learning, or GAN are black-box
models so they neither provide meaningful interpretation of sample selection in
DS nor stability on different domains. On the contrary, this work proposes a
novel model-agnostic instance sampling method for DS by influence function
(IF), namely REIF. Our method identifies favorable/unfavorable instances in the
bag based on IF, then does dynamic instance sampling. We design a fast
influence sampling algorithm that reduces the computational complexity from
$\mathcal{O}(mn)$ to $\mathcal{O}(1)$, with analyzing its robustness on the
selected sampling function. Experiments show that by simply sampling the
favorable instances during training, REIF is able to win over a series of
baselines that have complicated architectures. We also demonstrate that REIF
can support interpretable instance selection.

27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph