@inproceedings{deshmukh-etal-2021-unsupervised-chunking,
title = "Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach",
author = "Deshmukh, Anup Anand and
Zhang, Qianqiu and
Li, Ming and
Lin, Jimmy and
Mou, Lili",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.307",
doi = "10.18653/v1/2021.findings-emnlp.307",
pages = "3626--3634",
abstract = "In this paper, we address unsupervised chunking as a new task of syntactic structure induction, which is helpful for understanding the linguistic structures of human languages as well as processing low-resource languages. We propose a knowledge-transfer approach that heuristically induces chunk labels from state-of-the-art unsupervised parsing models; a hierarchical recurrent neural network (HRNN) learns from such induced chunk labels to smooth out the noise of the heuristics. Experiments show that our approach largely bridges the gap between supervised and unsupervised chunking.",
}
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<abstract>In this paper, we address unsupervised chunking as a new task of syntactic structure induction, which is helpful for understanding the linguistic structures of human languages as well as processing low-resource languages. We propose a knowledge-transfer approach that heuristically induces chunk labels from state-of-the-art unsupervised parsing models; a hierarchical recurrent neural network (HRNN) learns from such induced chunk labels to smooth out the noise of the heuristics. Experiments show that our approach largely bridges the gap between supervised and unsupervised chunking.</abstract>
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%0 Conference Proceedings
%T Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach
%A Deshmukh, Anup Anand
%A Zhang, Qianqiu
%A Li, Ming
%A Lin, Jimmy
%A Mou, Lili
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F deshmukh-etal-2021-unsupervised-chunking
%X In this paper, we address unsupervised chunking as a new task of syntactic structure induction, which is helpful for understanding the linguistic structures of human languages as well as processing low-resource languages. We propose a knowledge-transfer approach that heuristically induces chunk labels from state-of-the-art unsupervised parsing models; a hierarchical recurrent neural network (HRNN) learns from such induced chunk labels to smooth out the noise of the heuristics. Experiments show that our approach largely bridges the gap between supervised and unsupervised chunking.
%R 10.18653/v1/2021.findings-emnlp.307
%U https://aclanthology.org/2021.findings-emnlp.307
%U https://doi.org/10.18653/v1/2021.findings-emnlp.307
%P 3626-3634
Markdown (Informal)
[Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach](https://aclanthology.org/2021.findings-emnlp.307) (Deshmukh et al., Findings 2021)
ACL