One implementation of the paper "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

1. pytorch==1.7.1
2. 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}
}