Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Team \'UFAL at CMCL 2022 Shared Task: Figuring out the correct recipe
  for predicting Eye-Tracking features using Pretrained Language Models   Eye-Tracking data is a very useful source of information to study cognition
and especially language comprehension in humans. In this paper, we describe our
systems for the CMCL 2022 shared task on predicting eye-tracking information.
We describe our experiments with pretrained models like BERT and XLM and the
different ways in which we used those representations to predict four
eye-tracking features. Along with analysing the effect of using two different
kinds of pretrained multilingual language models and different ways of pooling
the tokenlevel representations, we also explore how contextual information
affects the performance of the systems. Finally, we also explore if factors
like augmenting linguistic information affect the predictions. Our submissions
achieved an average MAE of 5.72 and ranked 5th in the shared task. The average
MAE showed further reduction to 5.25 in post task evaluation.

11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat