MLMA_hate_speech

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MLMA_hate_speech

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Dataset and code of our EMNLP 2019 paper "Multilingual and Multi-Aspect Hate Speech Analysis"

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# Dataset and code of our EMNLP 2019 Paper (Multilingual and Multi-Aspect Hate Speech Analysis)
If you use our dataset or our guidelines please cite this paper:

@inproceedings{ousidhoum-etal-multilingual-hate-speech-2019,
    		title = "Multilingual and Multi-Aspect Hate Speech Analysis",
    		author = "Ousidhoum, Nedjma
             		and Lin, Zizheng
             		and Zhang, Hongming
            		and Song, Yangqiu
            		and Yeung, Dit-Yan",
    			booktitle = "Proceedings of EMNLP",
    		year = "2019",
    		publisher =	"Association for Computational Linguistics",
	}

## Update
- If you need the individual labels of some data instances (unfortunately I could not find all the batches on the cloud years later), please send me an email OusidhoumN(at)cardiff(dot)ac(dot)uk
 
- The dataset is available on HuggingFace https://huggingface.co/datasets/nedjmaou/MLMA_hate_speech

## Clarification
The multi-labeled tasks are *the hostility type of the tweet* and the *annotator's sentiment*. (We kept labels on which at least two annotators agreed.)

## Taxonomy
In further experiments that involved binary classification tasks of the hostility/hate/abuse type, we considered single-labeled *normal* instances to be *non-hate/non-toxic* and all the other instances to be *toxic*.

## Dataset
Our dataset is composed of three csv files sorted by language. They contain the tweets and the annotations described in our paper:

the hostility type *(column: tweet sentiment)*

hostility directness *(column: directness)*

target attribute *(column: target)*

target group *(column: group)*

annotator's sentiment *(column: annotator sentiment)*.

## Experiments

To replicate our experiments, please follow the guidelines below.

### Requirements
Python 3.6 onwards,

dyNET 0.0.0 and its dependencies (follow the instructions on https://dynet.readthedocs.io/en/latest/python.html).

[On a side note, when you install DyNet make sure that you are using CUDA 9 and CUDNN for CUDA 9. I used the following command:

CUDNN_ROOT=/path/to/conda/pkgs/cudnn-7.3.1-cuda10.0_0 \
		BACKEND=/path/to/conda/pkgs/cudatoolkit-10.0.130-0 \
		pip install git+https://github.com/clab/dynet#egg=dynet

Using CUDA 10 will generate an error when calling DyNet for GPUs.]

Cross-lingual word embeddings (Babylon or MUSE. The reported results have been run using Babylon.)

### Python files

- annotated_data_processing.py contains a normalization function that cleans the content of the tweets.

- constants.py defines constants used across all the files.

- utils.py utility functions for data processing.

- baseline_classifiers.py allows you to run majority voting and logistic regression by calling:

run_majority_voting(train_filename, dev_filename, test_filename, attribute) 
		
 or

run_logistic_regression(train_filename, dev_filename, test_filename, attribute)
	  
 on csv files of the same form of the dataset.

predictors.py contains classes for sequence predictors and layers.

run_sluice_net.py: Script to train, load, and evaluate SluiceNetwork.

sluice_net.py: The main logic for the SluiceNetwork (Ruder et al. 2017). More details on the implementation of Sluice networks can be found <a href="https://github.com/sebastianruder/sluice-networks">here</a>.

### How to run the program
To save and load the trained model, you need to create a directory (e.g., model/), and specify the name of the created directory when using --model-dir argument in the command line.

To save the log files of the training and evaluation, you need to create a directory (e.g., log/), and specify the name of the created directory when using --log-dir argumnet in the command line.

#### Example:
		python run_sluice_net.py --dynet-autobatch 1  --dynet-gpus 3 --dynet-seed 123 \
                          --h-layers 1 \
                         --cross-stitch\
                         --num-subspaces 2 --constraint-weight 0.1 \
                         --constrain-matrices 1 2 --patience 3 \
                         --languages ar en fr \
			 --test-languages ar en fr \
                         --model-dir model/ --log-dir log/\
                         --task-names annotator_sentiment sentiment directness group target \
			 --train-dir '/path/to/train' \
                         --dev-dir '/path/to/dev' \
                         --test-dir 'path/to/test' \
                         --embeds babylon --h-dim 200 \
			 --cross-stitch-init-scheme imbalanced \
			 --threshold 0.1

### NB 
- The meaning of each argument can be found in run_sluice_net.py

- '--task_names' refers to a list of task names (e.g: annotator_sentiment).

- '--languages' refers to the language dataset which will be used in the training.

- 'test-languages' can only be the subset of '--languages'.

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