Automatic Classification of News Subjects in Broadcast News: Application to a Gender Bias Representation Analysis

Automatic Classification of News Subjects in Broadcast News: Application to a Gender Bias Representation Analysis About Source code for the Interpseech 2024 paper about automatic classifcation of news subjects in broadcast news. The code contains evaluation scripts, models training and interfence source. Dataset The annotated dataset contains about 03h44min of broadcast news, with 605 dialogues for the Test set. The dataset can be downloaded at the following URL: https://www.ina.fr/recherche/dataset-project, under the name is24_news_topic. Evaluation scripts The scripts to eval your own models are included in the folder evaluation/. You will need an original copy of the dataset (see above.). You may use the following script: evaluation/eval \ --reference ORIGINAL_DATASET_FOLDER \ --prediction PRED_FOLDER/predictions.json \ --subset dev # dev or test The results will be saved inside PRED_FOLDER folder, with the name results.json. You need to format your predictions into the following format: { "DIALOGUE_ID_1": { "text": "input text for that dialogue (this field is not required in your output file, but it allow you to easily browse the file while reading the predicted output, and manually see if what your model predicted)", "class__ARTS/CULTURE/ENTERTAINMENT": false, "class__COMMERCIAL": false, "class__CRIME/LAW/JUSTICE": true, "class__DISASTER/ACCIDENT": false, "class__ECONOMY/BUSINESS/FINANCE": false, "class__EDUCATION": false, "class__ENVIRONMENTAL_ISSUE": false, "class__HEALTH": false, "class__LABOUR": false, "class__LIFESTYLE/LEISURE": false, "class__OTHER": false, "class__POLITICS": false, "class__RELIGION/BELIEF": false, "class__SCIENCE/TECHNOLOGY": false, "class__SOCIAL_ISSUE": false, "class__SPORT": false, "class__UNREST/CONFLICTS/WAR": true, "class__WEATHER": false }, "DIALOGUE_ID_2": { "..." } } Models The source code of training and inference of the models presented in the paper are included in the folder models/ (models/BERT and models/Mixtral). BERT finetuning The script to finetune all models presented in the paper is in models/BERT/train_all. It calls the train script. To generate the predictions of a specific model, use models/BERT/predict. You can then use evaluation/eval to get metric scores of your model. Mixtral The prompt used for the Mixtral model is in the file models/Mixtral/prompt.txt. Use the script models/Mixtral/inference_mixtral to generate the outputs of Mixtral (or an other model) base on this prompt and input dialogues. Then, call parse_safe_json to convert the output of the model into a valid JSON format, which you can then use with generate_prediction_dataset to create a valid training/validation/testing dataset. You then can use evaluation/eval to evaluate Mixtral response if the inference was on the dataset dialogues, or models/BERT/train to finetune BERT on these annotations. Installation You need python3, pip3. pip3 install -r requirements.txt Citation If you use this corpus or the source code of this repository, please cite the following article: @inproceedings{pelloin2024automatic, title = {Automatic Classification of News Subjects in Broadcast News: Application to a Gender Bias Representation Analysis}, author = {Valentin Pelloin and Lena Dodson and \'Emile Chapuis and Nicolas Hervé and David Doukhan}, booktitle = {Proc. InterSpeech 2024}, month = 9, year = 2024, address = "Kos Island, Greece", }