This is the code for our COLING 2020 paper:
Fact vs. Opinion: the Role of Argumentation Features in News Classification
Tariq Alhindi, Smaranda Muresan, Daniel Preotiuc-Pietro
It has a script for training rnn+bert model in a python script (train_rnn_bert.py) or jupyter notebook (rnn+bert.ipynb), which are equivalent. It also has a script for training the svm model and extracting BERT embedding features.
To train a rnn+bert model, you need:
- fine-tune a bert sentence-level classifier for argument component tagging (claim, premise, other), which can be done using huggingface script for text classification
- extract bert embeddings by running a code similar to extract_bert_features.py
- train the rnn+bert model using argumentation features and embeddings by running a code similar to train_rnn_bert.py
Current scripts are not currently ready for use as is. Modification of data and model directories is needed. I will aim to provide a more usable version of the scripts soon.