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arXiv · 1811.01382

Neural CRF transducers for sequence labeling

Kai Hu·Zhijian Ou·Min Hu·Junlan Feng
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Abstract

Conditional random fields (CRFs) have been shown to be one of the most successful approaches to sequence labeling. Various linear-chain neural CRFs (NCRFs) are developed to implement the non-linear node potentials in CRFs, but still keeping the linear-chain hidden structure. In this paper, we propose NCRF transducers, which consists of two RNNs, one extracting features from observations and the other capturing (theoretically infinite) long-range dependencies between labels. Different sequence labeling methods are evaluated over POS tagging, chunking and NER (English, Dutch). Experiment results show that NCRF transducers achieve consistent improvements over linear-chain NCRFs and RNN transducers across all the four tasks, and can improve state-of-the-art results.

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Kai Hu, Zhijian Ou, Min Hu, Junlan Feng. 2018-11-04. Neural CRF transducers for sequence labeling. https://arxiv.org/abs/1811.01382

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