Code for the paper "Combining Textual Features for the Detection of Hateful and Offensive Language"

Related tags

Deep Learning HASOC-2021---Hate-Speech-Detection
Overview

The repository provides the source code for the paper "Combining Textual Features for the Detection of Hateful and Offensive Language" submitted to HASOC 2021 English Subtask 1A.

Publication

Arxiv

Installation (requires >=Python 3.6 )

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
download the 'resources.zip' file here: https://drive.google.com/file/d/1X88cMrLVpAcJd5Z4Gg6MfTLclIuGF-d6/view?usp=sharing
extract the content of 'resources.zip'

Training and Evaluation on HASOC datasets (2019, 2020, 2021)

Execute the following command to train and evaluate the model. The evaluation results are saved under the folder 'results'.

python main.py -c config.json

Optimizing Hyperparameters

The "config.json" file contains hyperparameters that can be changed to train different variants of the model.

{
  "base_dir": "",
  "batch_size": 64,
  "epochs": 20,
  "epoch_patience": 5,
  "bert_model_dir": "resources/hatebert",
  "monitor": "loss",
  "tweet_text_seq_len": 80,
  "tweet_text_char_len": 128,
  "char_size": 29,
  "max_learning_rate": 0.001,
  "end_learning_rate": 0.0000001,
  "rnn_type": "lstm",
  "rnn_layer_size": 200,
  "text_models": ["char_emb", "bert", "hate_words"],
  "normalize_text": true,
  "dataset_year": "2021",
  "optimizer": "adam",
  "text_use_attention": false,
  "oversample": true,
  "feature_normalization_layer_size": 512,
  "min_feature_normalization_layer_size": 64
}

bert_model_dir

"bert_model_dir": "resources/hatebert"
     OR
"bert_model_dir": "resources/bert-base"

dataset_year

"dataset_year": "2019"
	OR
"dataset_year": "2020"
	OR
"dataset_year": "2021"

text_models

"text_models": ["hate_words"]
	OR
"text_models": ["bert"]
	OR
"text_models": ["char_emb"]
	OR
"text_models": ["char_emb", "bert", "hate_words"]

rnn_type

"rnn_type": "lstm"
	OR
"rnn_type": "gru"
	OR
"rnn_type": "bi-gru"
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