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        <title>Neural Structural Correspondence Learning for Domain Adaptation</title>
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        <namePart type="given">Yftah</namePart>
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            <title>Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)</title>
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            <namePart type="given">Roger</namePart>
            <namePart type="family">Levy</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
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    <abstract>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 (NNs). Our model is a three-layer NN that learns to encode the non-pivot 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. We experiment with the task of cross-domain sentiment classification on 16 domain pairs and show substantial improvements over strong baselines.</abstract>
    <identifier type="citekey">ziser-reichart-2017-neural</identifier>
    <identifier type="doi">10.18653/v1/K17-1040</identifier>
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        <date>2017-08</date>
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%0 Conference Proceedings
%T Neural Structural Correspondence Learning for Domain Adaptation
%A Ziser, Yftah
%A Reichart, Roi
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F ziser-reichart-2017-neural
%X 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 (NNs). Our model is a three-layer NN that learns to encode the non-pivot 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. We experiment with the task of cross-domain sentiment classification on 16 domain pairs and show substantial improvements over strong baselines.
%R 10.18653/v1/K17-1040
%U https://aclanthology.org/K17-1040
%U https://doi.org/10.18653/v1/K17-1040
%P 400-410

Markdown (Informal)

[Neural Structural Correspondence Learning for Domain Adaptation](https://aclanthology.org/K17-1040) (Ziser & Reichart, CoNLL 2017)

• Neural Structural Correspondence Learning for Domain Adaptation (Ziser & Reichart, CoNLL 2017)

ACL

• Yftah Ziser and Roi Reichart. 2017. Neural Structural Correspondence Learning for Domain Adaptation. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 400–410, Vancouver, Canada. Association for Computational Linguistics.