Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Structural Correspondence Learning for Domain Adaptation   Domain adaptation, adapting models from domains rich in labeled training data
to domains poor in such data, is a fundamental NLP challenge. We introduce a
neural network model that marries together ideas from two prominent strands of
research on domain adaptation through representation learning: structural
correspondence learning (SCL, (Blitzer et al., 2006)) and autoencoder neural
networks. Particularly, our model is a three-layer neural network that learns
to encode the nonpivot features of an input example into a low-dimensional
representation, so that the existence of pivot features (features that are
prominent in both domains and convey useful information for the NLP task) in
the example can be decoded from that representation. The low-dimensional
representation is then employed in a learning algorithm for the task. Moreover,
we show how to inject pre-trained word embeddings into our model in order to
improve generalization across examples with similar pivot features. On the task
of cross-domain product sentiment classification (Blitzer et al., 2007),
consisting of 12 domain pairs, our model outperforms both the SCL and the
marginalized stacked denoising autoencoder (MSDA, (Chen et al., 2012)) methods
by 3.77% and 2.17% respectively, on average across domain pairs.

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The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
2
The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: physics
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The categories of this paper are: nlin
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The categories of this paper are: eess