Neural-EDU-Segmentation

A toolkit for segmenting Elementary Discourse Units (clauses). We implement it as is described in our EMNLP paper: Toward Fast and Accurate Neural Discourse Segmentation

Requirements

Python 3.5
Tensorflow>=1.5.0
allennlp>=0.4.2
See requirements.txt for the full list of packages

Data

We cannot provide the complete RST-DT corpus due to the LDC copyright. So we only put several samples in ./data/rst/ to test the our code and show the data structure.

If you want to train or evaluate our model on RST-DT, you need to download the data manually and put it in the same folder. Then run the following command to preprocess the data and create the vocabulary:

python run.py --prepare

Evaluate the model on RST-DT:

We provide the vocabulary and a well-trained model in the ./data/ folder. You can evaluate the performance of this model after preparing the RST-DT data as mentioned above:

python run.py --evaluate --test_files ../data/rst/preprocessed/test/*.preprocessed

The performance of current model should be as follows:

'precision': 0.9176470588235294, 'recall': 0.975, 'f1': 0.9454545454545454}

Note that this is slightly better than the results we reported in the paper, since we re-trained the model and there is some randomness here.

Train a new model

You can use the following command to train the model from scratch:

python run.py --train

Hyper-parameters and other training settings can be modified in config.py.

Segmenting raw text into EDUs

You can segment files with raw text into EDUs:

python run.py --segment --input_files ../data/rst/TRAINING/wsj_110*.out --result_dir ../data/results/

The segmented result for each file will be saved to the --result_dir folder with the same name. Each EDU is written as a line.

Citation

Please cite the following paper if you use this toolkit in your work:

@inproceedings{wang2018edu,
  title={Toward Fast and Accurate Neural Discourse Segmentation},
  author={Wang, Yizhong and Li, Sujian and Yang, Jingfeng},
  booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  pages={962--967},
  year={2018}
}