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        <title>Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification</title>
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    <name type="personal">
        <namePart type="given">Xilun</namePart>
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    <name type="personal">
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        <namePart type="family">Athiwaratkun</namePart>
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    <name type="personal">
        <namePart type="given">Claire</namePart>
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    <name type="personal">
        <namePart type="given">Kilian</namePart>
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    <abstract>In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjoy such an abundance of labeled data. To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN1) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exist. ADAN has two discriminative branches: a sentiment classifier and an adversarial language discriminator. Both branches take input from a shared feature extractor to learn hidden representations that are simultaneously indicative for the classification task and invariant across languages. Experiments on Chinese and Arabic sentiment classification demonstrate that ADAN significantly outperforms state-of-the-art systems.</abstract>
    <identifier type="citekey">chen-etal-2018-adversarial</identifier>
    <identifier type="doi">10.1162/tacl_a_00039</identifier>
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        <date>2018</date>
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            <start>557</start>
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%0 Journal Article
%T Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification
%A Chen, Xilun
%A Sun, Yu
%A Athiwaratkun, Ben
%A Cardie, Claire
%A Weinberger, Kilian
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F chen-etal-2018-adversarial
%X In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjoy such an abundance of labeled data. To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN1) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exist. ADAN has two discriminative branches: a sentiment classifier and an adversarial language discriminator. Both branches take input from a shared feature extractor to learn hidden representations that are simultaneously indicative for the classification task and invariant across languages. Experiments on Chinese and Arabic sentiment classification demonstrate that ADAN significantly outperforms state-of-the-art systems.
%R 10.1162/tacl_a_00039
%U https://aclanthology.org/Q18-1039
%U https://doi.org/10.1162/tacl_a_00039
%P 557-570

Markdown (Informal)

[Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification](https://aclanthology.org/Q18-1039) (Chen et al., TACL 2018)

• Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification (Chen et al., TACL 2018)

ACL

• Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie, and Kilian Weinberger. 2018. Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification. Transactions of the Association for Computational Linguistics, 6:557–570.