Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Domain Adaptation for Machine Reading Comprehension   In this paper, we focus on unsupervised domain adaptation for Machine Reading
Comprehension (MRC), where the source domain has a large amount of labeled
data, while only unlabeled passages are available in the target domain. To this
end, we propose an Adversarial Domain Adaptation framework (AdaMRC), where
($i$) pseudo questions are first generated for unlabeled passages in the target
domain, and then ($ii$) a domain classifier is incorporated into an MRC model
to predict which domain a given passage-question pair comes from. The
classifier and the passage-question encoder are jointly trained using
adversarial learning to enforce domain-invariant representation learning.
Comprehensive evaluations demonstrate that our approach ($i$) is generalizable
to different MRC models and datasets, ($ii$) can be combined with pre-trained
large-scale language models (such as ELMo and BERT), and ($iii$) can be
extended to semi-supervised learning.

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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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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: math