Modal Dependency Parsing via Language Model Priming

Jiarui Yao; Nianwen Xue; Bonan Min

NAACL 2022long4 citations

Modal Dependency Parsing via Language Model Priming

Abstract

The task of modal dependency parsing aims to parse a text into its modal dependency structure, which is a representation for the factuality of events in the text. We design a modal dependency parser that is based on priming pre-trained language models, and evaluate the parser on two data sets. Compared to baselines, we show an improvement of 2.6% in F-score for English and 4.6% for Chinese. To the best of our knowledge, this is also the first work on Chinese modal dependency parsing.

BibTeX

@inproceedings{yao-etal-2022-modal,
    title = "Modal Dependency Parsing via Language Model Priming",
    author = "Yao, Jiarui  and
      Xue, Nianwen  and
      Min, Bonan",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.211/",
    doi = "10.18653/v1/2022.naacl-main.211",
    pages = "2913--2919"
}