README.md

Toxicity detection w/ and w/o context

• Concerning comments existing in a thread.
• Context information:
  • The parent comment.
  • The discussion topic.
• The large dataset is included in the data folder in the form of two CSV files.
  • gn.csv comprises the out of context annotations.
  • gc.csv comprises the in-context annotations.
• The small dataset will be included soon.

Word embeddings

• You will need to add a folder embeddings when using pre-trained embeddings.
  • For example, GloVe embeddings.

Building the datasets

Create random splits:

python experiments.py --create_random_splits 10

Downsample the two categories (one per dataset) to make the datasets equibalanced while equally sized:

python experiments.py --create_balanced_datasets

Then, create 10 random splits:

python experiments.py --create_random_splits 10 --use_balanced_datasets True

Running a classifier

Run a simple bi-LSTM by:

nohup python experiments.py --with_context_data False --with_context_model "RNN:OOC" --repeat 10 > rnn.ooc.log &

• You can train it also in IC data, by changing the related argument.
  • If you call "RNN:INC1", the same LSTM will be trained, but another LSTM will encode the parent text (IC data required) and concatenate the two encoded texts before the dense layers on the top.
  • If you call "BERT:OOC1" you have a simple BERT.
  • If you call "BERT:OOC2" you concatenate the parent text (IC data required) with a SEPARATED token.
  • If you call "BERT:CA" you extend BERT:OOC1 with the LSTM encoded parent text, similarly to the RNN:INC1.

The names are messy, but these will hopefully change.

The article

• Presented at ACL'20
• Link to arXiv
• Please cite:

@misc{pavlopoulos2020toxicity, title={Toxicity Detection: Does Context Really Matter?}, author={John Pavlopoulos and Jeffrey Sorensen and Lucas Dixon and Nithum Thain and Ion Androutsopoulos}, year={2020}, eprint={2006.00998}, archivePrefix={arXiv}, primaryClass={cs.CL}}