Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Span-based Question Answering Systems with Coarsely Labeled
  Data   We study approaches to improve fine-grained short answer Question Answering
models by integrating coarse-grained data annotated for paragraph-level
relevance and show that coarsely annotated data can bring significant
performance gains. Experiments demonstrate that the standard multi-task
learning approach of sharing representations is not the most effective way to
leverage coarse-grained annotations. Instead, we can explicitly model the
latent fine-grained short answer variables and optimize the marginal
log-likelihood directly or use a newly proposed \emph{posterior distillation}
learning objective. Since these latent-variable methods have explicit access to
the relationship between the fine and coarse tasks, they result in
significantly larger improvements from coarse supervision.

34
The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph