This repository contains the data and source code used in NAACL 2021 main conference paper Adversarial Learning for Zero-Shot Stance Detection on Social Media.
python 3.7.6
transformers 3.4.0
pytorch 1.5.1
numpy 1.18.1
pandas 1.0.3
scipy 1.4.1
Create an environment with dependencies specified in stance_local_env.yml (note that this can take some time):
conda env create -f stance_local_env.ymlActivate the new environment:
conda activate stance_envTo deactivate an active environment, use
conda deactivatecd src/
Create folder data and within it create a folder named resources.
In data/resources, place pretrained GloVe word embeddings and topic dictionary (which maps topics in training data to indices).
Run
python train_and_eval_model.py --mode "train" --config_file <config_name> --trn_data <train_data> --dev_data <dev_data> --score_key <score_key> --topics_vocab <topic_dictionary> --mode train
For example:
python train_and_eval_model.py --mode "train" --config_file data/config-0.txt --trn_data data/twitter_testDT_seenval/development_setup/train.csv --dev_data data/twitter_testDT_seenval/development_setup/validation.csv --score_key f_macro --topics_vocab twitter-topic-TRN-semi-sup.vocab.pkl --mode train
Score key is evaluated on the development data and used for saving the best model across epochs.
Config file for TOAD should follow the format of our example TOAD config file - src/config_example_toad.txt
To evaluate a saved model on test_data, run
python train_and_eval_model.py --mode "eval" --config_file <config_name> --trn_data <train_data> --dev_data <test_data> --topics_vocab <topic_dictionary> --saved_model_file_name <saved_model_file_name> --mode eval
For example:
python train_and_eval_model.py --mode "eval" --config_file data/config-0.txt --trn_data data/twitter_testDT_seenval/development_setup/train.csv --dev_data data/twitter_testDT_seenval/test_setup/test.csv --saved_model_file_name data/checkpoints/DT_checkpoint.tar --topics_vocab twitter-topic-TRN-semi-sup.vocab.pkl --mode eval
Run
python train_and_eval_model.py --mode "train" --config_file <config_name> --trn_data <train_data> --dev_data <dev_data> --score_key <score_key>
Config file should follow the format of our example BiCond config file - src/config_example_bicond.txt
Run
python train_and_eval_model.py --mode "train" --config_file <config_name> --trn_data <train_data> --dev_data <dev_data> --score_key <score_key>
Run
python hyperparam_selection.py -m 1 -s <config_file_for_hyperparam_tuning> -k <score_key>
Config file should follow the format of our example TOAD hyperparameter search config file - src/hyperparam-twitter-adv.txt
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