RST Parsing from Scratch

This repository contains the source code of our paper "RST Parsing from Scratch" in NAACL 2021.

Requirements

• python: 3.7
• pytorch: 1.4
• transformers: 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 and dummy_format_data/sample_rawtext_data_format
• For other parsing models: Refer to create_sample_dummy_format_data.py and dummy_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 = "",
doi = "",
pages = "xx--xx",}
}