One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing".

Related tags

Deep Learning DMRST_Parser
Overview

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}
}
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Comments
  • Dimension out of range at pred = torch.argmax(outputs, dim=1)

    Dimension out of range at pred = torch.argmax(outputs, dim=1)

    Hi,

    Congrats on the great work. I've been using your trained model to classify arguments in over 800 news in both spanish and portuguese, I further modified the original MUL_main_Infer.py code to match my needs (the only file I modified). I made it capable of loading a group of texts in a directory one by one, splitting them into sentences, and then predicting the labels for each sentence to finally save the output as a single csv file.

    I am predicting for 800 txt files now. It works very well until I add more than the first 2 files to the directory. However, the error seems to rise up whenever I choose to add all the files in it. According to the debugging file, the error is in the pred = torch.argmax(outputs, dim=1) line within the module.py file.

    This is the error in question:

      File "C:\Users\diego\Escritorio\DMRST\MUL_main_Infer.py", line 114, in <module>
    input_sentences, all_segmentation_pred, all_tree_parsing_pred = inference(model, bert_tokenizer, Test_InputSentences, batch_size)
  File "C:\Users\diego\Escritorio\DMRST\MUL_main_Infer.py", line 63, in inference
    _, _, SPAN_batch, _, predict_EDU_breaks = model.TestingLoss(input_sen_batch, input_EDU_breaks=None, LabelIndex=None,
  File "C:\Users\diego\Escritorio\DMRST\model_depth.py", line 184, in TestingLoss
    EncoderOutputs, Last_Hiddenstates, _, predict_edu_breaks = self.encoder(input_sentence, input_EDU_breaks, is_test=use_pred_segmentation)
  File "C:\Users\diego\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\diego\Escritorio\DMRST\module.py", line 81, in forward
    predict_edu_breaks = self.segmenter.test_segment_loss(embeddings.squeeze())
  File "C:\Users\diego\Escritorio\DMRST\module.py", line 355, in test_segment_loss
    pred = torch.argmax(outputs, dim=1).detach().cpu().numpy().tolist()
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

    Can you please help me solving this issue? I would be extremely thankful for that.

    opened by diegopaucarv 3
  • the relation numbers

    the relation numbers

    hello, i just want to know the relation nums , because i find the relations nums in your paper is 18, but i just find 16 in your references, the 2 is span and same-unit?

    opened by jing86736238 1
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