Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach
  to Grade Essays Using Gaze Behaviour   The gaze behaviour of a reader is helpful in solving several NLP tasks such
as automatic essay grading. However, collecting gaze behaviour from readers is
costly in terms of time and money. In this paper, we propose a way to improve
automatic essay grading using gaze behaviour, which is learnt at run time using
a multi-task learning framework. To demonstrate the efficacy of this multi-task
learning based approach to automatic essay grading, we collect gaze behaviour
for 48 essays across 4 essay sets, and learn gaze behaviour for the rest of the
essays, numbering over 7000 essays. Using the learnt gaze behaviour, we can
achieve a statistically significant improvement in performance over the
state-of-the-art system for the essay sets where we have gaze data. We also
achieve a statistically significant improvement for 4 other essay sets,
numbering about 6000 essays, where we have no gaze behaviour data available.
Our approach establishes that learning gaze behaviour improves automatic essay
grading.

23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an