Human vs. Machine Perceptions on Immigration Stereotypes Link to paper

This repository contains the code for classification models of racial stereotypes in a Spanish corpus (StereoHoax-ES), using both hard and soft labels.

Disclaimer: the original repository was used with more corpora and options than the ones presented on the article above. For example:

• Task 1 refers to stereotype prediction (yes/no) [this paper].
• Task 2 was the stereotype impliciteness classification (imp/exp), see DETESTS-Dis: DETEction and classification of racial STereotypes in Spanish - Learning with Disagreement.

Table of Contents

• Programs and folder structure
  • Main programs
  • Utils
• StereoHoax-ES corpus
• Results
• Reproduce
  • Setup
  • Split
  • Baselines
  • BERTs Fine-tuning
    • Hyper-parameters
  • Figures

Programs and folder structure

Main programs

• baselines.py: Creates the baselines for the tasks.
• context_soft.py Adds the context attributes and soft-labels to the datasets.
• create_figures.py Create figues, stored in results/figures.
• create_metrics.py: Computes the metrics for the results files.
• preprocess_split.py: Preprocesses and splits the corpus as needed.

Utils

• config.py: Contains feature names and constants.

• data_pred.py: Does the data processing and predictions for the fine-tuned models.

• fine_tuning.py Utils for the fine-tuning notebook.

• io.py: Parses inputs and outputs.

• plots.py: Plot utils.

• results.py: Contains a class to store the results and functions to compute metrics.

• split.py: Utils for split notebooks.

• trainers.py: Different HF Trainers and main train function.

• scripts: This folder contains bash scripts for reproducibility.

The following notebooks are included:

• split_stereohoax.ipynb splits the StereoHoax corpus (see split).
• fine_tuning_hard_and_soft.ipynb has the fine-tuning of the BERT models. It was originally run on free Google Colab with a T4 GPU. It was since ported to be used on a local machine.

StereoHoax-ES corpus

If you are interested in the corpus for research purposes please contact the authors, and we will provide the zip password.

• stereoHoax-ES_goldstandard.csv is the original data.
• stereohoax_unaggregated.csv has the 3 unaggregated annotations for "stereotype","implicit", "contextual" and "url".
• train_val_split.csv, train_split.csv, val_split.csv and test_split.csv are the split sets, also with the unaggregated annotations.
  1. context_soft.py creates a _context_soft version for each one.
  2. preprocess_split takes train_val_split_context_soft.csv and test_split_context_soft.csv as inputs to create train.csv and test.csv.

Results

Predictions for each model are stored in the results folder along with the gold standard. Each model's results are separated into different CSV files, with the predictions for each feature being named '_pred'.

The overall metrics for all models are shown in the results/metrics folder.

These metrics can be recreated using the create_metrics.py script.

Reproduce

Setup

To set up the necessary environment and download required models, run scripts/setup.sh.

Split

For the baselines, the data is simply split into train and test sets. For the BERT models, a validation set is also created.

For the StereoHoax corpus, the following splits are created: 70% train, 10% validation, and 20% test. Different racial hoaxes are separated into different sets to avoid data leakage and preserve the distribution of stereotypes. The split is performed using split_stereohoax.py.

split_stereohoax.py works in the following way:

1. Finds combination of hoaxes that reach 70%, 20% and 10% of the data.
2. Finds which of these combinations has the most similar topic distribution to the original data.

The resulting splits are the following:

• Train_val - 80% = Train + val (used for baselines)
• Train - 70%: 'SP003', 'SP013', 'SP064', 'SP054', 'SP070', 'SP017', 'SP067', 'SP043', 'SP036', 'SP048'
• Val - 10%: 'SP005', 'SP065', 'SP052', 'SP055', 'SP068'
• Test - 20%: 'SP057', 'SP015', 'SP049', 'SP047', 'SP010', 'SP014', 'SP009', 'SP027', 'SP040', 'SP020', 'SP023', 'SP008', 'SP031'

The percentage of the whole dataset that each racial hoax (RH) contributes to the splits is the following:

Train = {
    'SP067': 0.93,
    'SP043': 27.97,
    'SP036': 8.32,
    'SP048': 0.06,
    'SP064': 14.73,
    'SP003': 16.69,
    'SP054': 0.02,
    'SP070': 0.04,
    'SP013': 0.02,
    'SP017': 0.07,
    'sum':  68.85,
}
Validation = {
    'SP052': 1.72,
    'SP068': 3.87,
    'SP005': 0.06,
    'SP065': 2.13,
    'SP055': 3.33,
    'sum':  11.10,
}
Test = {
    'SP010': 0.19,
    'SP008': 1.31,
    'SP014': 1.42,
    'SP027': 5.12,
    'SP015': 3.72,
    'SP009': 0.50,
    'SP040': 0.79,
    'SP031': 0.24,
    'SP020': 1.70,
    'SP023': 0.34,
    'SP047': 3.44,
    'SP049': 1.03,
    'SP057': 0.24,
    'sum':  20.04,
}

Baselines

train.csv and test.csv are used for the baselines. To obtain them run scripts/preprocess.sh, which calls preprocess_split.py.

The following baselines are considered:

• All-zeros
• All-ones
• Weighted random classifier
• TFIDF (with only unigrams) + linear SVC (Support Vector Classifier)
• TFIDF (with n-grams with sizes 1 to 3) + linear SVC
• FastText vectorization + linear SVC

They can be run with: python baselines.py

BERTs Fine-tuning

To fine-tune the BERT models for both corpora, run the fine_tuning_hard_and_soft.ipynb notebook with the adequate inputs.

The BERT models used for these tasks include:

• 'PlanTL-GOB-ES/roberta-base-bne' (RoBERTa-BNE). Trained with data from the National Library of Spain (Biblioteca Nacional de España).
• 'dccuchile/bert-base-spanish-wwm-cased' (BETO). Trained with the data specified in https://github.com/josecannete/spanish-corpora.
• 'pysentimiento/robertuito-base-uncased' (RoBERTuito). Tained with twitter data.

Hyper-parameters

The models with hard labels uses a learning rate of 2e-5, while the model with soft-labels uses 1e-5.

We keep the model with lowest loss.

Figures

To create all the figures run:

python create_metrics.py
python create_figures.py