Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dialect Identification in Nuanced Arabic Tweets Using Farasa
  Segmentation and AraBERT   This paper presents our approach to address the EACL WANLP-2021 Shared Task
1: Nuanced Arabic Dialect Identification (NADI). The task is aimed at
developing a system that identifies the geographical location(country/province)
from where an Arabic tweet in the form of modern standard Arabic or dialect
comes from. We solve the task in two parts. The first part involves
pre-processing the provided dataset by cleaning, adding and segmenting various
parts of the text. This is followed by carrying out experiments with different
versions of two Transformer based models, AraBERT and AraELECTRA. Our final
approach achieved macro F1-scores of 0.216, 0.235, 0.054, and 0.043 in the four
subtasks, and we were ranked second in MSA identification subtasks and fourth
in DA identification subtasks.

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The categories of this paper are: nlin
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The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con