Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DiaNet: BERT and Hierarchical Attention Multi-Task Learning of
  Fine-Grained Dialect   Prediction of language varieties and dialects is an important language
processing task, with a wide range of applications. For Arabic, the native
tongue of ~ 300 million people, most varieties remain unsupported. To ease this
bottleneck, we present a very large scale dataset covering 319 cities from all
21 Arab countries. We introduce a hierarchical attention multi-task learning
(HA-MTL) approach for dialect identification exploiting our data at the city,
state, and country levels. We also evaluate use of BERT on the three tasks,
comparing it to the MTL approach. We benchmark and release our data and models.

36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
3
The categories of this paper are: physics