DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing

Introduction

One implementation of the paper “DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing”.
Users can apply it to parse the input text from scratch, and get the EDU segmentations and the parsed tree structure.
The model supports both sentence-level and document-level RST discourse parsing.
This repo and the pre-trained model is only for research use.

Package Requirements

pytorch==1.7.1
transformers==4.8.2

Supported Languages

We trained and evaluated the model with the multilingual collection of RST discourse treebanks, and it natively supports 6 languages: English, Portuguese, Spanish, German, Dutch, Basque. Interested users can also try other languages.

Data Format

[Input] InputSentence: The input document/sentence, and the raw text will be tokenizaed and encoded by the xlm-roberta-base language backbone. ‘|| ‘ denotes the EDU boundary positions.
- Although the report, || which has released || before the stock market opened, || didn’t trigger the 190.58 point drop in the Dow Jones Industrial Average, || analysts said || it did play a role in the market’s decline. ||
[Output] EDU_Breaks: The indices of the EDU boundary tokens, including the last word of the sentence.
- [2, 5, 10, 22, 24, 33]
[Output] tree_parsing_output: The model outputs of the discourse parsing tree follow this format.
- (1:Satellite=Contrast:4,5:Nucleus=span:6) (1:Nucleus=Same-Unit:3,4:Nucleus=Same-Unite:4) (5:Satellite=Attribution:5,6:Nucleus=span:6) (1:Satellite=span:1,2:Nucleus=Elaboration:3) (2:Nucleus=span:2,3:Satellite=Temporal:3)

How to use it for parsing

Put the text paragraph to the file ./data/text_for_inference.txt.
Run the script MUL_main_Infer.py to obtain the RST parsing result. See the script for detailed model output.
We recommend users to run the parser on a GPU-equipped environment.

Citation

@article{liu2021dmrst,
  title={DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing},
  author={Liu, Zhengyuan and Shi, Ke and Chen, Nancy F},
  journal={arXiv preprint arXiv:2110.04518},
  year={2021}
}

@inproceedings{liu2020multilingual,
  title={Multilingual Neural RST Discourse Parsing},
  author={Liu, Zhengyuan and Shi, Ke and Chen, Nancy},
  booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
  pages={6730--6738},
  year={2020}
}