yandachen/GI_2019
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
README 1. Data Preprocessing Preprocess tweet text: preprocess.py Create vocabulary set and capture vocabulary information: create_vocab.py Create cross validation dataset: create_dataset.py Embed sentence to vectors with word embedding: compare_embeddings.py, create_embeddings.py, create_matrix_from_embeddings.py 2. Model Definition, Training/Evaluation Infrastructure CNN model architecture: model_def.py LSTM Attention model architecture: LSTM_attn.py CNN Training/Evaluation infrastructure: nn_experiment.py LSTM Attention Training/Evaluation infrastructure: model.py Load training/evaluation test data: data_loader.py Read experiment results and calculate macro F1: read_results.py, generate_summaries_from_test_nn.py Please refer to section 6 for the training/evaluation command line arguments. 3. Performance Analysis Evaluate average/majority-vote aggression, loss, other, macro F1-score from experiment dirs: evaluate_performance.py To calculate the average/majority-vote aggression, loss, other, macro F1-score of all models, run "python3 evaluate_performance.py" 4. Rationale Rank Analysis Leave-one-out analysis: LIME.py To calculate models' rationale rank and the influence rank of stopword unigrams discussed in the paper, run "python3 LIME.py" 5. Adversarial Dataset Generation ELMo language model implementation: bi_lm.py, ELMo.py, create_ELMo_embedding.py, embedwELMo.py Generate adversarial dataset and evaluate number of flip predictions: adversarial_dataset.py To generate aggression adversarial dataset for the unigrams listed in the paper, run "python3 adversarial_dataset.py" 6. Evaluation of Models listed in the Paper Models' experiment dir and prediction thresholds: model_info.py Models' data loading format: model_test_data_loader.py Script to train all models discussed in paper: train_final_models.py Script to load all trained models: load_final_models.py To train a model: run "python3 train_final_models.py <model_id>"
About
No description, website, or topics provided.
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published