@inproceedings{yang-etal-2021-neural,
title = "Neural Bi-Lexicalized {PCFG} Induction",
author = "Yang, Songlin and
Zhao, Yanpeng and
Tu, Kewei",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.209",
doi = "10.18653/v1/2021.acl-long.209",
pages = "2688--2699",
abstract = "Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction. However, to reduce computational complexity, they make a strong independence assumption on the generation of the child word and thus bilexical dependencies are ignored. In this paper, we propose an approach to parameterize L-PCFGs without making implausible independence assumptions. Our approach directly models bilexical dependencies and meanwhile reduces both learning and representation complexities of L-PCFGs. Experimental results on the English WSJ dataset confirm the effectiveness of our approach in improving both running speed and unsupervised parsing performance.",
}
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<abstract>Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction. However, to reduce computational complexity, they make a strong independence assumption on the generation of the child word and thus bilexical dependencies are ignored. In this paper, we propose an approach to parameterize L-PCFGs without making implausible independence assumptions. Our approach directly models bilexical dependencies and meanwhile reduces both learning and representation complexities of L-PCFGs. Experimental results on the English WSJ dataset confirm the effectiveness of our approach in improving both running speed and unsupervised parsing performance.</abstract>
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%0 Conference Proceedings
%T Neural Bi-Lexicalized PCFG Induction
%A Yang, Songlin
%A Zhao, Yanpeng
%A Tu, Kewei
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F yang-etal-2021-neural
%X Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction. However, to reduce computational complexity, they make a strong independence assumption on the generation of the child word and thus bilexical dependencies are ignored. In this paper, we propose an approach to parameterize L-PCFGs without making implausible independence assumptions. Our approach directly models bilexical dependencies and meanwhile reduces both learning and representation complexities of L-PCFGs. Experimental results on the English WSJ dataset confirm the effectiveness of our approach in improving both running speed and unsupervised parsing performance.
%R 10.18653/v1/2021.acl-long.209
%U https://aclanthology.org/2021.acl-long.209
%U https://doi.org/10.18653/v1/2021.acl-long.209
%P 2688-2699
Markdown (Informal)
[Neural Bi-Lexicalized PCFG Induction](https://aclanthology.org/2021.acl-long.209) (Yang et al., ACL-IJCNLP 2021)
ACL
- Songlin Yang, Yanpeng Zhao, and Kewei Tu. 2021. Neural Bi-Lexicalized PCFG Induction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2688–2699, Online. Association for Computational Linguistics.