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        <title>Semi-supervised Structured Prediction with Neural CRF Autoencoder</title>
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        <namePart type="given">Xiao</namePart>
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    <abstract>In this paper we propose an end-to-end neural CRF autoencoder (NCRF-AE) model for semi-supervised learning of sequential structured prediction problems. Our NCRF-AE consists of two parts: an encoder which is a CRF model enhanced by deep neural networks, and a decoder which is a generative model trying to reconstruct the input. Our model has a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We developed a variation of the EM algorithm for optimizing both the encoder and the decoder simultaneously by decoupling their parameters. Our Experimental results over the Part-of-Speech (POS) tagging task on eight different languages, show that our model can outperform competitive systems in both supervised and semi-supervised scenarios.</abstract>
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%0 Conference Proceedings
%T Semi-supervised Structured Prediction with Neural CRF Autoencoder
%A Zhang, Xiao
%A Jiang, Yong
%A Peng, Hao
%A Tu, Kewei
%A Goldwasser, Dan
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F zhang-etal-2017-semi
%X In this paper we propose an end-to-end neural CRF autoencoder (NCRF-AE) model for semi-supervised learning of sequential structured prediction problems. Our NCRF-AE consists of two parts: an encoder which is a CRF model enhanced by deep neural networks, and a decoder which is a generative model trying to reconstruct the input. Our model has a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We developed a variation of the EM algorithm for optimizing both the encoder and the decoder simultaneously by decoupling their parameters. Our Experimental results over the Part-of-Speech (POS) tagging task on eight different languages, show that our model can outperform competitive systems in both supervised and semi-supervised scenarios.
%R 10.18653/v1/D17-1179
%U https://aclanthology.org/D17-1179/
%U https://doi.org/10.18653/v1/D17-1179
%P 1701-1711

Markdown (Informal)

[Semi-supervised Structured Prediction with Neural CRF Autoencoder](https://aclanthology.org/D17-1179/) (Zhang et al., EMNLP 2017)

• Semi-supervised Structured Prediction with Neural CRF Autoencoder (Zhang et al., EMNLP 2017)

ACL

• Xiao Zhang, Yong Jiang, Hao Peng, Kewei Tu, and Dan Goldwasser. 2017. Semi-supervised Structured Prediction with Neural CRF Autoencoder. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1701–1711, Copenhagen, Denmark. Association for Computational Linguistics.