Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Disentangled Representations of Texts with Application to
  Biomedical Abstracts   We propose a method for learning disentangled representations of texts that
code for distinct and complementary aspects, with the aim of affording
efficient model transfer and interpretability. To induce disentangled
embeddings, we propose an adversarial objective based on the (dis)similarity
between triplets of documents with respect to specific aspects. Our motivating
application is embedding biomedical abstracts describing clinical trials in a
manner that disentangles the populations, interventions, and outcomes in a
given trial. We show that our method learns representations that encode these
clinically salient aspects, and that these can be effectively used to perform
aspect-specific retrieval. We demonstrate that the approach generalizes beyond
our motivating application in experiments on two multi-aspect review corpora.

6
The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
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The categories of this paper are: nlin
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys