Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Online Multitask Learning of Behavioral Sentence Embeddings   Unsupervised learning has been an attractive method for easily deriving
meaningful data representations from vast amounts of unlabeled data. These
representations, or embeddings, often yield superior results in many tasks,
whether used directly or as features in subsequent training stages. However,
the quality of the embeddings is highly dependent on the assumed knowledge in
the unlabeled data and how the system extracts information without supervision.
Domain portability is also very limited in unsupervised learning, often
requiring re-training on other in-domain corpora to achieve robustness. In this
work we present a multitask paradigm for unsupervised contextual learning of
behavioral interactions which addresses unsupervised domain adaption. We
introduce an online multitask objective into unsupervised learning and show
that sentence embeddings generated through this process increases performance
of affective tasks.

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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
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The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: math
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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: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: physics