RST Parsing from Scratch
This repository contains the source code of our paper RST Parsing from Scratch in NAACL 2021.
Requirements
python
: 3.7pytorch
: 1.4transformers
: 3.0
Usage
To train a discourse parser:
./*_train.sh
To predict discourse tree:
./*_predict.sh
Data Format
- For end-to-end parsing from scratch (no sentence guidance):
we need to create the data with dummy edu_break and doc_structure. Refer to
create_sample_dummy_format_data.py
anddummy_format_data/sample_rawtext_data_format
- For other parsing models:
Refer to
create_sample_dummy_format_data.py
anddummy_format_data/sample_full_data_format
Citation
Please cite our paper if you found the resources in this repository useful.
@inproceedings{nguyen-etal-2021-rst-scratch,
title = "RST Parsing from Scratch",
author = "Nguyen, Thanh-Tung and
Nguyen, Xuan-Phi and
Joty, Shafiq and
Li, Xiaoli",
booktitle = "Proceedings of the 2021 Conference of the North {A}merican Chapter
of the Association for Computational Linguistics: Human Language Technologies,
Volume 1 (Long and Short Papers)",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2105.10861",
doi = "",
pages = "xx--xx",}
}