ToxiChat

Code and data for the EMNLP 2021 paper "Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts".

Install dependencies

conda env create -f environment.yml

Data

The train, dev, test split of the data are given in data/OC_S_post_thread/ folder

Offensive and Stance Classification models

Single instance Offensive Classification

NBOW model

We will train NBOW single sentence classification model initialized with GloVe embedding
To train NBOW model, you'd need to download and extract GloVe vectors into data/GloVe/ dir and then run python convert_glove_text_vectors_to_pkl.py from within the directory

• Training offensive classifier on OC_S_post_thread data
  python experiments/train_and_evaluate_NBOW_offensive_classifier.py -g data/GloVe/glove.6B.300d.pkl -td "{'OC_S':'data/OC_S_post_thread/'}" -s saved_models/OC_S_post_thread/NBOW_OC_S_offensive_e30 -o results/OC_S_post_thread/NBOW_OC_S_offensive_e30 -e 30 -dv 1 -t

BERT large cased model

• Training offensive classifier on OC_S_post_thread data
  python experiments/train_and_evaluate_BERT_offensive_classifier.py -e 8 -td "{'OC_S':'data/OC_S_post_thread/'}" -s saved_models/OC_S_post_thread/BERT_large_OC_S_offensive_e8 -o results/OC_S_post_thread/BERT_large_OC_S_offensive_e8 -t

Full Sequence Offensive Classification (DGPT)

We will train a DGPT model offensive classifier for the entire comment thread with EOS tokens used for sentence representations.

• Training offensive classifier on OC_S_post_thread data
  python experiments/train_and_evaluate_DGPT_offensive_classifier.py -e 12 -td "{'OC_S':'data/OC_S_post_thread/'}" -s saved_models/OC_S_post_thread/DGPT_medium_OC_S_offensive_e12 -o results/OC_S_post_thread/DGPT_medium_OC_S_offensive_e12 -t
• Training offensive classifier on OC_S_post_thread + SBF data
  python experiments/train_and_evaluate_DGPT_offensive_classifier.py -e 3 -td "{'OC_S':'data/OC_S_post_thread/', 'SBF':'data/SBF'}" -s saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e3 -o results/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e3 -t -dv 4

Stance Classification

Pairwise Stance Classification

NBOW model

We will train NBOW Sentence Pair classification model initialized with GloVe embedding

• Training Stance classifier on OC_S_post_thread_data (cross entropy)
  python experiments/train_and_evaluate_NBOW_pairwise_stance_classifier.py -g data/GloVe/glove.6B.300d.pkl -td "{'OC_S':'data/OC_S_post_thread/'}" -s saved_models/OC_S_post_thread/NBOW_OC_S_pairwise_stance_e30 -o results/OC_S_post_thread/NBOW_OC_S_pairwise_stance_e30 -e 30 -dv 1 -t

BERT large cased model

We will train Bert Sentence Pair classification model

• Training Stance classifier on OC_S_post_thread_data (cross entropy)
  python experiments/train_and_evaluate_BERT_pairwise_stance_classifier.py -e 8 -td "{'OC_S':'data/OC_S_post_thread/'}" -s saved_models/OC_S_post_thread/BERT_large_OC_S_pairwise_stance_e8 -o results/OC_S_post_thread/BERT_large_OC_S_pairwise_stance_e8 -t

Full Sequence Stance Classification

We will train a DGPT model stance classifier for the entire comment thread with EOS tokens used for sentence representations.

• Training Stance classifier on OC_S_post_thread_data (cross entropy)
  python experiments/train_and_evaluate_DGPT_stance_classifier.py -e 12 -td "{'OC_S':'data/OC_S_post_thread/'}" -s saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e12 -o results/OC_S_post_thread/DGPT_medium_OC_S_stance_e12 -t
• Training Stance classifier on OC_S_post_thread_data (Focal Loss)
  python experiments/train_and_evaluate_DGPT_stance_classifier.py -e 16 -td "{'OC_S':'data/OC_S_post_thread/'}" -s saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -o results/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -foc -lr 5e-5 -t

To download pretrained DGPT offensive and Stance (Focal) classifiers use the following link

Mitigating Offensive language using Controlled Text Generation

Dataset Preparation

We will first create a dataset of posts and comments from all of the reddit. Then we will create comment trees from these posts and comments and label them with our stance and offensive classifiers

Downloading the reddit posts and comments dumps

1. Download the reddit comments and submissions dumps from August(08) to October(10), 2019 in the data folder

   mkdir -p data/reddit_dumps/comments_compressed
   cd data/reddit_dumps/comments_compressed
   wget -nc https://files.pushshift.io/reddit/comments/RC_2019-10.zst
   wget -nc https://files.pushshift.io/reddit/comments/RC_2019-09.zst
   wget -nc https://files.pushshift.io/reddit/comments/RC_2019-08.zst
   wget -nc https://files.pushshift.io/reddit/comments/RC_2019-07.zst
   wget -nc https://files.pushshift.io/reddit/comments/RC_2019-06.zst
   wget -nc https://files.pushshift.io/reddit/comments/RC_2019-05.zst
   cd ..
   mkdir posts_compressed
   cd posts_compressed
   wget -nc https://files.pushshift.io/reddit/submissions/RS_2019-10.zst
   wget -nc https://files.pushshift.io/reddit/submissions/RS_2019-09.zst
   wget -nc https://files.pushshift.io/reddit/submissions/RS_2019-08.zst
   wget -nc https://files.pushshift.io/reddit/submissions/RS_2019-07.zst
   wget -nc https://files.pushshift.io/reddit/submissions/RS_2019-06.zst
   wget -nc https://files.pushshift.io/reddit/submissions/RS_2019-05.zst
   cd ../../
   

Create posts and comments sample

• python extract_reddit_posts.py -f data/reddit_dumps/posts_compressed/RS_2019-10.zst data/reddit_dumps/posts_compressed/RS_2019-09.zst data/reddit_dumps/posts_compressed/RS_2019-08.zst data/reddit_dumps/posts_compressed/RS_2019-07.zst data/reddit_dumps/posts_compressed/RS_2019-06.zst data/reddit_dumps/posts_compressed/RS_2019-05.zst -p 0.8 -o data/reddit_dumps/posts/all_mitigating_sample/
• python extract_reddit_comments_for_posts.py -f data/reddit_dumps/comments_compressed/RC_2019-05.zst data/reddit_dumps/comments_compressed/RC_2019-06.zst data/reddit_dumps/comments_compressed/RC_2019-07.zst data/reddit_dumps/comments_compressed/RC_2019-08.zst data/reddit_dumps/comments_compressed/RC_2019-09.zst data/reddit_dumps/comments_compressed/RC_2019-10.zst -p data/reddit_dumps/posts/all_mitigating_sample/all_subreddit_posts.jsonl -o data/reddit_dumps/comments/all_mitigating_sample/

Create threads from posts and comments sample

python create_post_comment_trees_from_all_reddit_sample.py -ip data/reddit_dumps/posts/all_mitigating_sample/all_subreddit_posts.jsonl -ic data/reddit_dumps/comments/all_mitigating_sample/all_subreddit_post_related_comments.jsonl -mc 3 -o data/reddit_dumps/post_comment_threads/all_mitigating_sample/

Split the post comment threads into 4 splits

python split_threads_into_files.py -i data/reddit_dumps/post_comment_threads/all_mitigating_sample/all_reddit_post_and_comments_3_threads.pkl -o data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/ -n 4

Predict separately for each split

• python predict_DGPT_stance_on_post_comment_trees.py -i data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/split_0.pkl -sm saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -om saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e2 -o data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/ -s data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/split_0_preds.pkl
• python predict_DGPT_stance_on_post_comment_trees.py -i data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/split_1.pkl -sm saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -om saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e2 -o data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/ -s data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/split_1_preds.pkl
• python predict_DGPT_stance_on_post_comment_trees.py -i data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/split_2.pkl -sm saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -om saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e2 -o data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/ -s data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/split_2_preds.pkl
• python predict_DGPT_stance_on_post_comment_trees.py -i data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/split_3.pkl -sm saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -om saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e2 -o data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/ -s data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/split_3_preds.pkl

Merge predictions

python merge_Off_Stance_predictions.py -i data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/ -n 4 -o data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/merged_split_predictions.pkl

Create CTG fine-tuning dataset from post_comment threads with stance and offensive labels

python get_fine_tuning_subsets_from_label_predicted_convs.py -i data/reddit_dumps/post_comment_threads/all_mitigating_sample/splits/predictions_both/merged_split_predictions.pkl -o data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/

Fine-tune DGPT medium model for different CTG experiments

DAPT

CTG using DAPT i.e. simply training on the subset we care about

1. Off Control [SAFE] subset (DAPT - [S])

python experiments/CTG_DGPT_finetuner.py -so [SAFE] -t data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/off_control_train.pkl -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/off_control_dev.pkl -s saved_models/CTG/Off_control_DGPT_safe_subset -o results/CTG/Off_control_DGPT_safe_subset -e 3

2. Safe Stance Control [NO-STANCE] subset (DAPT - [S][N])

python experiments/CTG_DGPT_finetuner.py -so [NO-STANCE] -t data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/safe_stance_control_train.pkl -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/safe_stance_control_dev.pkl -s saved_models/CTG/safe_stance_control_DGPT_no_stance_subset -o results/CTG/safe_stance_control_DGPT_no_stance_subset -e 3

ATCON

CTG using control labels

1. Offensive Label Control (ATCON [S])

python experiments/CTG_DGPT_finetuner.py -t data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/off_control_train.pkl -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/off_control_dev.pkl -s saved_models/CTG/Off_control_DGPT -o results/CTG/Off_control_DGPT -e 3 -dv 100

2. Stance Label Control (Safe) (ATCON [N])

python experiments/CTG_DGPT_finetuner.py -t data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/safe_stance_control_train.pkl -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/safe_stance_control_dev.pkl -s saved_models/CTG/safe_stance_control_DGPT -o results/CTG/safe_stance_control_DGPT -e 3

3. Both Offensive and Stance Label Control (both) (ATCON [S][N])

python experiments/CTG_DGPT_finetuner.py -t data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/both_control_train.pkl -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/both_control_dev.pkl -s saved_models/CTG/both_control_DGPT -o results/CTG/both_control_DGPT -e 3

Generate Responses on test set using CTG models

Control labels [OFF]/[SAFE] and [AGREE]/[NO-STANCE]

• Baseline No Control
  python generate_CTG_responses_and_make_off_and_stance_predictions.py -m microsoft/DialoGPT-medium -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/test_threads.pkl -sm saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -om saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e3 -n 1 -bs 10 -o results/CTG/DGPT/test_threads_replies_and_off_stance_preds.pkl
• DAPT Offensive Control Safe Subset (DAPT - [S])
  python generate_CTG_responses_and_make_off_and_stance_predictions.py -m saved_models/CTG/Off_control_DGPT_safe_subset -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/test_threads.pkl -sm saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -om saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e2 -n 1 -bs 10 -o results/CTG/Off_control_DGPT/DAPT_Off_control_safe_subset_test_threads_replies_and_off_stance_preds.pkl
• DAPT Safe Stance Control No-Stance Subset (DAPT - [S][N])
  python generate_CTG_responses_and_make_off_and_stance_predictions.py -m saved_models/CTG/safe_stance_control_DGPT_no_stance_subset -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/test_threads.pkl -sm saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -om saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e2 -n 1 -bs 10 -o results/CTG/safe_stance_control_DGPT/DAPT_safe_stance_control_no_stance_subset_test_threads_replies_and_off_stance_preds.pkl
• Offensive Control (ATCON - [S])
  python generate_CTG_responses_and_make_off_and_stance_predictions.py -m saved_models/CTG/Off_control_DGPT -p [SAFE] -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/test_threads.pkl -sm saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -om saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e2 -n 1 -bs 10 -o results/CTG/Off_control_DGPT/Off_control_test_threads_safe_replies_and_off_stance_preds.pkl
• Stance Control (Safe) (ATCON - [N])
  python generate_CTG_responses_and_make_off_and_stance_predictions.py -m saved_models/CTG/safe_stance_control_DGPT -p [NO-STANCE] -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/test_threads.pkl -sm saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -om saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e2 -n 1 -bs 10 -o results/CTG/safe_stance_control_DGPT/safe_stance_control_test_threads_no_stance_replies_and_off_stance_preds.pkl
• Both Control (ATCON - [S][N])
  python generate_CTG_responses_and_make_off_and_stance_predictions.py -m saved_models/CTG/both_control_DGPT -p [SAFE][NO-STANCE] -d data/reddit_dumps/post_comment_threads/CTG_experiments/all_mitigating_sample/final/test_threads.pkl -sm saved_models/OC_S_post_thread/DGPT_medium_OC_S_stance_e16_focal_lr5e_5 -om saved_models/OC_S_post_thread/DGPT_medium_OC_S_and_SBF_offensive_e2 -n 1 -bs 10 -o results/CTG/both_control_DGPT/both_control_test_threads_safe_no_stance_replies_and_off_stance_preds.pkl

Automatic evalaution of CTG test predictions

python automatic_evaluation_of_CTG_test_predictions.py -mg "[('DGPT medium baseline', 'results/CTG/DGPT/test_threads_replies_and_off_stance_preds.pkl'), ('ATCON - [S]', 'results/CTG/Off_control_DGPT/Off_control_test_threads_safe_replies_and_off_stance_preds.pkl'), ('ATCON [N]', 'results/CTG/safe_stance_control_DGPT/safe_stance_control_test_threads_no_stance_replies_and_off_stance_preds.pkl'), ('ATCON [N][S]', 'results/CTG/both_control_DGPT/both_control_test_threads_safe_no_stance_replies_and_off_stance_preds.pkl'), ('DAPT [S]', 'results/CTG/Off_control_DGPT/DAPT_Off_control_safe_subset_test_threads_replies_and_off_stance_preds.pkl'), ('DAPT [S][N]', 'results/CTG/safe_stance_control_DGPT/DAPT_safe_stance_control_no_stance_subset_test_threads_replies_and_off_stance_preds.pkl')]" -o results/CTG/auto_eval/

Citation

@article{baheti2021just,
  title={Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts},
  author={Baheti, Ashutosh and Sap, Maarten and Ritter, Alan and Riedl, Mark},
  journal={arXiv preprint arXiv:2108.11830},
  year={2021}
}