@inproceedings{wei-etal-2020-span,
title = "A Span-based Linearization for Constituent Trees",
author = "Wei, Yang and
Wu, Yuanbin and
Lan, Man",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.299",
doi = "10.18653/v1/2020.acl-main.299",
pages = "3267--3277",
abstract = "We propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all spans ending with that split point, and then predicts a tree span from them. Compared with global models, our model is fast and parallelizable. Different from previous local models, our linearization method is tied on the spans directly and considers more local features when performing span prediction, which is more interpretable and effective. Experiments on PTB (95.8 F1) and CTB (92.4 F1) show that our model significantly outperforms existing local models and efficiently achieves competitive results with global models.",
}
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<abstract>We propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all spans ending with that split point, and then predicts a tree span from them. Compared with global models, our model is fast and parallelizable. Different from previous local models, our linearization method is tied on the spans directly and considers more local features when performing span prediction, which is more interpretable and effective. Experiments on PTB (95.8 F1) and CTB (92.4 F1) show that our model significantly outperforms existing local models and efficiently achieves competitive results with global models.</abstract>
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%0 Conference Proceedings
%T A Span-based Linearization for Constituent Trees
%A Wei, Yang
%A Wu, Yuanbin
%A Lan, Man
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F wei-etal-2020-span
%X We propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all spans ending with that split point, and then predicts a tree span from them. Compared with global models, our model is fast and parallelizable. Different from previous local models, our linearization method is tied on the spans directly and considers more local features when performing span prediction, which is more interpretable and effective. Experiments on PTB (95.8 F1) and CTB (92.4 F1) show that our model significantly outperforms existing local models and efficiently achieves competitive results with global models.
%R 10.18653/v1/2020.acl-main.299
%U https://aclanthology.org/2020.acl-main.299
%U https://doi.org/10.18653/v1/2020.acl-main.299
%P 3267-3277
Markdown (Informal)
[A Span-based Linearization for Constituent Trees](https://aclanthology.org/2020.acl-main.299) (Wei et al., ACL 2020)
ACL
- Yang Wei, Yuanbin Wu, and Man Lan. 2020. A Span-based Linearization for Constituent Trees. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3267–3277, Online. Association for Computational Linguistics.