This project includes the dataset and source code for the paper Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays. Detail informatin please refers to the paper.
./data/Ch_train.json # Chinese trianing dataset
./data/Ch_test.json # Chinese test dataset
./data/En_train.json # English training dataset
./data/En_test.json # English test dataset
./embd/new_embeddings2.txt #Tencent pre-trained word embeddings from https://ai.tencent.com/ailab/nlp/en/embedding.html
./embd/bert-base-uncased-word_embeddings.pkl # BERT-base-uncased word embeddings from the embeddings module of bert-base-uncased
./train.py # training code for Chinese dataset
./train_e.py # training code for English dataset
./train_ef.py # training code for English dataset with features
./test.py
# Chinese_test(model_dir) for Chinese test
# English_test(model_dir) for English test
# English_test_ft(model_dir) for English test with features
./model/roles/DiSA_ch/DiSA_ch.pk # The DiSA model for Chinese dataset
./model/e_roles_4/DiSA_en4/DiSA_en4.pk # TheDiSA model for English dataset(4 classes)
./model/e_roles_3/DiSA_en3/DiSA_en3.pk # The DiSA model for English dataset(3 classes)
./model/e_roles_3/DiSA_ft_en3/DiSA_ft_en3.pk # The DiSA model with features for English dataset(3 classes)
parameter 'p_embd' setting
- 'add' RelativeSPE
- 'embd_b' PosEmbedding
- 'embd_c' Sinusoidal
The code and dataset are released with this paper:
@inproceedings{song-etal-2020-discourse,
title = "Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays",
author = "Song, Wei and
Song, Ziyao and
Fu, Ruiji and
Liu, Lizhen and
Cheng, Miaomiao and
Liu, Ting",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.225",
pages = "2820--2830",
}