Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach

Anup Anand Deshmukh; Qianqiu Zhang; Ming Li; Jimmy Lin; Lili Mou

EMNLP 2021finding6 citations

Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach

Anup Anand Deshmukh, Qianqiu Zhang, Ming Li, Jimmy Lin, Lili Mou

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.

BibTeX

@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"
}