@inproceedings{hu-etal-2020-investigation,
title = "An Investigation of Potential Function Designs for Neural {CRF}",
author = "Hu, Zechuan and
Jiang, Yong and
Bach, Nguyen and
Wang, Tao and
Huang, Zhongqiang and
Huang, Fei and
Tu, Kewei",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.236/",
doi = "10.18653/v1/2020.findings-emnlp.236",
pages = "2600--2609",
abstract = "The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling. In this paper, we investigate a series of increasingly expressive potential functions for neural CRF models, which not only integrate the emission and transition functions, but also explicitly take the representations of the contextual words as input. Our extensive experiments show that the decomposed quadrilinear potential function based on the vector representations of two neighboring labels and two neighboring words consistently achieves the best performance."
}
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<abstract>The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling. In this paper, we investigate a series of increasingly expressive potential functions for neural CRF models, which not only integrate the emission and transition functions, but also explicitly take the representations of the contextual words as input. Our extensive experiments show that the decomposed quadrilinear potential function based on the vector representations of two neighboring labels and two neighboring words consistently achieves the best performance.</abstract>
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%0 Conference Proceedings
%T An Investigation of Potential Function Designs for Neural CRF
%A Hu, Zechuan
%A Jiang, Yong
%A Bach, Nguyen
%A Wang, Tao
%A Huang, Zhongqiang
%A Huang, Fei
%A Tu, Kewei
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F hu-etal-2020-investigation
%X The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling. In this paper, we investigate a series of increasingly expressive potential functions for neural CRF models, which not only integrate the emission and transition functions, but also explicitly take the representations of the contextual words as input. Our extensive experiments show that the decomposed quadrilinear potential function based on the vector representations of two neighboring labels and two neighboring words consistently achieves the best performance.
%R 10.18653/v1/2020.findings-emnlp.236
%U https://aclanthology.org/2020.findings-emnlp.236/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.236
%P 2600-2609
Markdown (Informal)
[An Investigation of Potential Function Designs for Neural CRF](https://aclanthology.org/2020.findings-emnlp.236/) (Hu et al., Findings 2020)
ACL