./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)

    @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",
    }