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        <title>Variational Sequential Labelers for Semi-Supervised Learning</title>
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    <name type="personal">
        <namePart type="given">Mingda</namePart>
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    <name type="personal">
        <namePart type="given">Qingming</namePart>
        <namePart type="family">Tang</namePart>
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    <name type="personal">
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        <namePart type="family">Livescu</namePart>
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    <name type="personal">
        <namePart type="given">Kevin</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration from word prediction objectives commonly used in learning word embeddings. The labeler helps inject discriminative information into the latent space. We explore several latent variable configurations, including ones with hierarchical structure, which enables the model to account for both label-specific and word-specific information. Our models consistently outperform standard sequential baselines on 8 sequence labeling datasets, and improve further with unlabeled data.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1020</identifier>
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        <date>2018-oct-nov</date>
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            <start>215</start>
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%0 Conference Proceedings
%T Variational Sequential Labelers for Semi-Supervised Learning
%A Chen, Mingda
%A Tang, Qingming
%A Livescu, Karen
%A Gimpel, Kevin
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F chen-etal-2018-variational
%X We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration from word prediction objectives commonly used in learning word embeddings. The labeler helps inject discriminative information into the latent space. We explore several latent variable configurations, including ones with hierarchical structure, which enables the model to account for both label-specific and word-specific information. Our models consistently outperform standard sequential baselines on 8 sequence labeling datasets, and improve further with unlabeled data.
%R 10.18653/v1/D18-1020
%U https://aclanthology.org/D18-1020
%U https://doi.org/10.18653/v1/D18-1020
%P 215-226

Markdown (Informal)

[Variational Sequential Labelers for Semi-Supervised Learning](https://aclanthology.org/D18-1020) (Chen et al., EMNLP 2018)

• Variational Sequential Labelers for Semi-Supervised Learning (Chen et al., EMNLP 2018)

ACL

• Mingda Chen, Qingming Tang, Karen Livescu, and Kevin Gimpel. 2018. Variational Sequential Labelers for Semi-Supervised Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 215–226, Brussels, Belgium. Association for Computational Linguistics.