Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AutoRC: Improving BERT Based Relation Classification Models via
  Architecture Search   Although BERT based relation classification (RC) models have achieved
significant improvements over the traditional deep learning models, it seems
that no consensus can be reached on what is the optimal architecture. Firstly,
there are multiple alternatives for entity span identification. Second, there
are a collection of pooling operations to aggregate the representations of
entities and contexts into fixed length vectors. Third, it is difficult to
manually decide which feature vectors, including their interactions, are
beneficial for classifying the relation types. In this work, we design a
comprehensive search space for BERT based RC models and employ neural
architecture search (NAS) method to automatically discover the design choices
mentioned above. Experiments on seven benchmark RC tasks show that our method
is efficient and effective in finding better architectures than the baseline
BERT based RC model. Ablation study demonstrates the necessity of our search
space design and the effectiveness of our search method.

9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph