Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Analyzing Curriculum Learning for Sentiment Analysis along Task
  Difficulty, Pacing and Visualization Axes   While Curriculum Learning (CL) has recently gained traction in Natural
language Processing Tasks, it is still not adequately analyzed. Previous works
only show their effectiveness but fail short to explain and interpret the
internal workings fully. In this paper, we analyze curriculum learning in
sentiment analysis along multiple axes. Some of these axes have been proposed
by earlier works that need more in-depth study. Such analysis requires
understanding where curriculum learning works and where it does not. Our axes
of analysis include Task difficulty on CL, comparing CL pacing techniques, and
qualitative analysis by visualizing the movement of attention scores in the
model as curriculum phases progress. We find that curriculum learning works
best for difficult tasks and may even lead to a decrement in performance for
tasks with higher performance without curriculum learning. We see that One-Pass
curriculum strategies suffer from catastrophic forgetting and attention
movement visualization within curriculum pacing. This shows that curriculum
learning breaks down the challenging main task into easier sub-tasks solved
sequentially.

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The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci