@inproceedings{shen-etal-2021-structformer,
title = "{S}truct{F}ormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling",
author = "Shen, Yikang and
Tay, Yi and
Zheng, Che and
Bahri, Dara and
Metzler, Donald and
Courville, Aaron",
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.559",
doi = "10.18653/v1/2021.acl-long.559",
pages = "7196--7209",
abstract = "There are two major classes of natural language grammars {---} the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of grammars, we introduce a novel model, StructFormer, that can induce dependency and constituency structure at the same time. To achieve this, we propose a new parsing framework that can jointly generate a constituency tree and dependency graph. Then we integrate the induced dependency relations into the transformer, in a differentiable manner, through a novel dependency-constrained self-attention mechanism. Experimental results show that our model can achieve strong results on unsupervised constituency parsing, unsupervised dependency parsing, and masked language modeling at the same time.",
}
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<abstract>There are two major classes of natural language grammars — the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of grammars, we introduce a novel model, StructFormer, that can induce dependency and constituency structure at the same time. To achieve this, we propose a new parsing framework that can jointly generate a constituency tree and dependency graph. Then we integrate the induced dependency relations into the transformer, in a differentiable manner, through a novel dependency-constrained self-attention mechanism. Experimental results show that our model can achieve strong results on unsupervised constituency parsing, unsupervised dependency parsing, and masked language modeling at the same time.</abstract>
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%0 Conference Proceedings
%T StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
%A Shen, Yikang
%A Tay, Yi
%A Zheng, Che
%A Bahri, Dara
%A Metzler, Donald
%A Courville, Aaron
%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 shen-etal-2021-structformer
%X There are two major classes of natural language grammars — the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of grammars, we introduce a novel model, StructFormer, that can induce dependency and constituency structure at the same time. To achieve this, we propose a new parsing framework that can jointly generate a constituency tree and dependency graph. Then we integrate the induced dependency relations into the transformer, in a differentiable manner, through a novel dependency-constrained self-attention mechanism. Experimental results show that our model can achieve strong results on unsupervised constituency parsing, unsupervised dependency parsing, and masked language modeling at the same time.
%R 10.18653/v1/2021.acl-long.559
%U https://aclanthology.org/2021.acl-long.559
%U https://doi.org/10.18653/v1/2021.acl-long.559
%P 7196-7209
Markdown (Informal)
[StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling](https://aclanthology.org/2021.acl-long.559) (Shen et al., ACL-IJCNLP 2021)
ACL