Semi-supervised Structured Prediction with Neural CRF Autoencoder

Xiao Zhang, Yong Jiang, Hao Peng, Kewei Tu, Dan Goldwasser


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.
Anthology ID:
D17-1179
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1701–1711
Language:
URL:
https://www.aclweb.org/anthology/D17-1179
DOI:
10.18653/v1/D17-1179
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1179.pdf