Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting BERT to improve aspect-based sentiment analysis performance
  on Persian language   Aspect-based sentiment analysis (ABSA) is a more detailed task in sentiment
analysis, by identifying opinion polarity toward a certain aspect in a text.
This method is attracting more attention from the community, due to the fact
that it provides more thorough and useful information. However, there are few
language-specific researches on Persian language. The present research aims to
improve the ABSA on the Persian Pars-ABSA dataset. This research shows the
potential of using pre-trained BERT model and taking advantage of using
sentence-pair input on an ABSA task. The results indicate that employing
Pars-BERT pre-trained model along with natural language inference auxiliary
sentence (NLI-M) could boost the ABSA task accuracy up to 91% which is 5.5%
(absolute) higher than state-of-the-art studies on Pars-ABSA dataset.

0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th