Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with
  Semantic Role Labeling   For over a decade, machine learning has been used to extract
opinion-holder-target structures from text to answer the question "Who
expressed what kind of sentiment towards what?". Recent neural approaches do
not outperform the state-of-the-art feature-based models for Opinion Role
Labeling (ORL). We suspect this is due to the scarcity of labeled training data
and address this issue using different multi-task learning (MTL) techniques
with a related task which has substantially more data, i.e. Semantic Role
Labeling (SRL). We show that two MTL models improve significantly over the
single-task model for labeling of both holders and targets, on the development
and the test sets. We found that the vanilla MTL model which makes predictions
using only shared ORL and SRL features, performs the best. With deeper analysis
we determine what works and what might be done to make further improvements for
ORL.

31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ