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Misinformation_Multitask_NE_Attn

Code for the paper "Misinformation detection using multi-task learning with mutual learning for novelty detection and emotion recognition"

Rina Kumari*, Nischal Ashok*, Tirthankar Ghoshal, Asif Ekbal

* denotes equal contribution

ipm_multitask_model

(Above) The schematic representation of the final architecture (Proposed Model).

To replicate the results please follow the following instructions.

Generating Novelty Results

Download the Glove-300 embeddings from https://nlp.stanford.edu/projects/glove/ and place it in the resources folder of the main directory and novelty_module/resources.

Preprocessing -

Run python preprocess_bytedance.py Run python preprocess_fnc.py And place the generated combined files in a folder data/quora_bd and data/quora_fnc_4ag_5dg respectively along with the test.txt and dev.txt from the data/quora folder

Preparing the datasets for evaluation

Run all_convert_txt.py and place the fnc results in the folder data/fnc_quora and bytedance results in data/bd_quora.py

  • ByteDance Dataset
  1. Training the model with the Quora-BD train data python novelty_module/train.py novelty_module/configs/main_quora.json5
  2. Generating the novelty aware embeddings using trained model (separately from train and test data) Note - Please change the name of the output representations and predictions file every time you run with a different dataset - to change line 122 and line 126 of the src/model_new.py file python novelty_module/evaluate.py novelty_module/models/quora_bd/benchmark/best.pt novelty_module/data/bd_quora/train.txt
  • FNC Dataset
  1. Training the model with the Quora-BD train data python novelty_module/train.py novelty_module/configs/main_quora_new.json5
  2. Generating the novelty aware embeddings using trained model (separately from train and test data) python novelty_module/evaluate.py novelty_module/models/quora_fnc_4ag_5dg/benchmark/best.pt novelty_module/data/fnc_quora/train_fnc_processed.txt

Please follow similar procedure for the other datasets.

Combining novelty results to get best results on FNC and FNID

  1. Make sure the paths in the file are right python novelty_module/novelty_fnc_results_combine.py python novelty_module/novelty_fnid_results_combine.py

Generating Emotion Results

Download the pre-trained BERT model from here - (https://github.com/google-research/bert) and unzip them inside the bert directory. In the paper, we use the cased base model.

Preparing the datasets

python fnc_data_prepare.py python bytedance_data_prepare.py

  1. Training the model with the Klinger dataset python bert_kling_new.py

  • ByteDance dataset Have to change the path in the code corresponding to the premise, hypothesis files of the train and test datasets (train_ag_dg_hyp.csv and test_ag_dg_hyp.csv) python bert_classifier_klinger.py

  • FNC dataset Have to change the path in the code corresponding to the premise, hypothesis files of the train and test datasets (train_ag_dg_hyp_fnc.csv and test_ag_dg_hyp_fnc.csv) python bert_classifier_klinger.py

Please follow the same procedure for the other datasets.

  1. Training the model with Goemotion dataset Please run the notebook goemotion_lstm.ipynb in the lstm_goemtions folder with the appropriate dataset input.

  2. Best Emotion To combine and find the best emotion labels Run the respective python files in the best_emotion folder

Proposed_Model (Folder) -

Contains the implementations of the final proposed model along with the supporting files

Baselines (Folder) -

Contains the implementations of the baselines along with the supporting files

Co-Ocurence_Matrices (Folder)

Contains code for generating co-occurance matrices which show the distribution of novelty and emotion labels with respect to the ground truth labels.

References: -

Our code is loosely based on the following
novelty_module - https://github.com/alibaba-edu/simple-effective-text-matching-pytorch
emotion_module - https://github.com/google-research/google-research/tree/master/goemotions