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The Moral Integrity Corpus (MIC)

This repository contains source code for the The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems by Caleb Ziems, Jane A. Yu, Yi-Chia Wang, Alon Y. Halevy, Diyi Yang

[Paper] | [Data Use Agreement] | [Data]

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

What is MIC?

Open-domain or "chit-chat" conversational agents often reflect insensitive, hurtful, or contradictory viewpoints that erode a user’s trust in the integrity of the system. Moral integrity is one important pillar for building trust.

MIC is a dataset that can help us understand chatbot behaviors through their latent values and moral statements. MIC contains 114k annotations, with 99k distinct "Rules of Thumb" (RoTs) that capture the moral assumptions of 38k chatbot replies to open-ended prompts. These RoTs represent diverse moral viewpoints, with the following distribution of underlying moral foundations:

• Care: wanting someone or something to be safe, healthy, and happy. (58k chatbot replies)
• Fairness: wanting to see individuals or groups treated equally or equitably. (24k)
• Liberty: wanting people to be free to make their own decisions. (22k)
• Loyalty: wanting unity and seeing people keep promises or obligations to an in-group. (22k)
• Authority: wanting to respect social roles, duties, privacy, peace, and order. (20k)
• Sanctity: wanting people and things to be clean, pure, innocent, and holy. (13k)

We can train encoder-decoder models to automatically generate RoT explanations for chatbot behaviors. This could facilitate explainable downstream applications. For example, we could train RL systems that demote chatbot replies which fall into certain moral classes or train safety classifiers that guide systems towards the desired behaviors, with sensitivity towards ideological and political difference.

In the RoT generation task, we find our best models match the quality, fluency, and relevance of human annotations, but they still generate irrelevant RoTs nearly 28% of the time. This suggests that the proposed generation task is not yet solved and that MIC can continue to serve as resource for ongoing work in developing morally-consistent conversational agents.

Where can I download the data?

If you have not already, please complete our short Data Use Agreement. Then follow this link to download MIC.csv.

Full Project Pipeline (annotation + experiments + analysis)

0. Project Environment

• CUDA, cudnn
• anaconda

1. Prepare separate environment for running GPT-Neo (requires old version of PyTorch) and that for generation

conda create --name neo python=3.7
conda activate neo
pip install -r requirements_neo.txt
conda deactivate

conda activate summarize
pip install -r requirements_summarize.txt
conda deactivate

2. Create main project environment

conda create --name morals python=3.7
conda activate morals
pip install -r requirements_morals.txt

3. Intstall PyTorch (be sure cudatoolkit matches your version of CUDA)

conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch

1. Build a Collection of Moral QA Pairs

Download data and resources

Download r/AskReddit Q/A data from the public Reddit-QA-Corpus as well as the Expanded Morality Lexicon by running

bash get_data.sh

Filter questions via lexicons

1. Extract moral foundations keywords from original Q/A pairs

python find_morals.py --column "questions"
python find_morals.py --column "answers"

2. Filter Q/A pairs so that both Q and the Reddit answer have at least one word in the Expanded Morality Lexicon, the Q has a question mark and does not contain the token 'reddit'

python filter_questions.py

Use chatbots to answer AskReddit prompts

Generate answers using BlenderBot. You can use the --start_idx and --end_idx flags to run the generations on only a subset of the prompts and parallelize.

python chatbot_generation/generate_answer.py --model_name blenderbot --indir "data/raw/Reddit-QA-Corpus/filtered/" --outdir "data/prepared/blenderbot_generations/" --use_gpu

Generate answers using DialoGPT. You can use the --start_idx and --end_idx flags to run the generations on only a subset of the prompts and parallelize.

python chatbot_generation/generate_answer.py --model_name dialoGPT --indir "data/raw/Reddit-QA-Corpus/filtered/" --outdir "data/prepared/dialoGPT_generations/" --use_gpu

Generate answers using GPT-Neo. You can use the --start_idx and --end_idx flags to run the generations on only a subset of the prompts and parallelize.

conda activate neo
python generate_answer.py --model_name "gpt-neo" --indir "data/raw/Reddit-QA-Corpus/filtered/" --outdir "data/prepared/gpt-neo_generations/" --use_gpu

Filter QA pairs via lexicons

1. Find moral words in the chatbot responses

python find_morals.py --column "blenderbot_A0" --indir "data/prepared/blenderbot_generations"

python find_morals.py --column "dialoGPT_A0" --indir "data/prepared/dialoGPT_generations"

python find_morals.py --column "gpt-neo_A0" --indir "data/prepared/gpt-neo_generations"

2. Filter via lexicons

python filter_qa.py --column "blenderbot_A0" --indir "data/prepared/blenderbot_generations"

python filter_qa.py --column "dialoGPT_A0" --indir "data/prepared/dialoGPT_generations"

python filter_qa.py --column "gpt-neo_A0" --indir "data/prepared/gpt-neo_generations"

Run the QAFiltering Task

Prepare input files for HIT using data from data/prepared/{model}_agg/filtered.csv, here swapping {model} for "blenderbot" or "dialoGPT" or "gpt-neo", adjusting also the start and end flags.

hit/Task_QAFiltering/prepare_hit.py --model {model} --start_idx 0 --end_idx 100

Train classifiers to filter QA pairs

1. Create train / dev / test splits for each of the chatbot distributions by running

hit/Task_QAFiltering/make_splits.py --model {model} --output "qa_filtering_clf/splits"

2. Tune classifiers via tuning_script.sh

bash tuning_script.sh 0 "sufficient" "qa_filtering_clf/splits/blenderbot" &
bash tuning_script.sh 1 "moral" "qa_filtering_clf/splits/blenderbot" &

bash tuning_script.sh 2 "sufficient" "qa_filtering_clf/splits/dialoGPT" &
bash tuning_script.sh 3 "moral" "qa_filtering_clf/splits/dialoGPT" &

bash tuning_script.sh 4 "sufficient" "qa_filtering_clf/splits/gpt-neo" &
bash tuning_script.sh 5 "moral" "qa_filtering_clf/splits/gpt-neo" &

3. Train the best performing model using qa_filtering_clf/sentence_pairs_clf.py and run it using python run_sentence_pairs_clf.py. For example:

qa_filtering_clf/sentence_pairs_clf.py --batchsize 16 --epochs 5 --lr "5e-5" --gpu 0 --label "dialoGPT_0" --input "qa_filtering_clf/splits/dialoGPT" --output "qa_filtering_clf/results_dialoGPT"

python qa_filtering_clf/run_sentence_pairs_clf.py --input "data/prepared/dialoGPT_generations_agg/filtered.csv" --output "qa_filtering_clf/dialoGPT_filtered_moral_predictions.txt" --sent2 "dialoGPT_A0" --path_to_model "qa_filtering_clf/results_dialoGPT/albert-base-v2-moral-16-5-5.pt"

Use classifier outputs to filter QA pairs

qa_filtering_clf/clf_filtering.py --model "blenderbot"
qa_filtering_clf/clf_filtering.py --model "dialoGPT"
qa_filtering_clf/clf_filtering.py --model "gpt-neo"

2. Run the Main RoT Annotation Task

Prepare hit files (choosing the start_idx and end_idx to indicate which portions of the )

python ./hit/Task_QAFiltering/prepare_hit.py --model "blenderbot" --start_idx 0 --end_idx 100 

Set up the Political Leaning Personality Test (after you modify the file with the proper API keys)

python ./hit/Qual_PoliticalLeaning/qual_request.py

Set up main Qualification Test (after you modify the file with the proper API keys)

python ./hit/Task_MoralEvaluation/qual_request.py

Run the MoralEvaluation.html task on MTurk, using input from ./hit/Task_QAFiltering/input, and saving output with the format ./hit/output/filtered_clf_sufficient_moral_blenderbot_0_100_FINISHED.csv

After running all annotation batches, prepare MIC

python ./hit/Task_MoralEvaluation/prepare_MIC.py --output "./data/mic/MIC.csv"

3. Train RoT Generation Models

Note:

To start at this step, download the MIC Data MIC.csv from the following link and place it in a new ./data/mic/ directory.

• Dropbox

Train the three generative models

CUDA_VISIBLE_DEVICES=0 python -m rot_generation.generate_rots --input "./data/mic/MIC.csv" --output "./rot_generation/output" --epochs 5 --model_checkpoint "gpt2" --gpu 0 --format_string "Q [answ] A [rot] ~ rot"

CUDA_VISIBLE_DEVICES=1 python -m rot_generation.generate_rots --input "./data/mic/MIC.csv" --output "./rot_generation/output" --epochs 5 --model_checkpoint "t5-small" --gpu 1 --format_string "Q [answ] A [rot] ~ rot"

CUDA_VISIBLE_DEVICES=2 python -m rot_generation.generate_rots --input "./data/mic/MIC.csv" --output "./rot_generation/output" --epochs 5 --model_checkpoint "facebook/bart-large" --gpu 2 --format_string "Q [answ] A [rot] ~ rot"

Decode the generative models

bash rot_generation/decode_all.sh "rot_generation/output/*" "./data/mic/MIC.csv" "Q [answ] A [rot] ~ rot" 1 "QA"

Run baseline retrieval generations

python rot_generation/retrieve_rots.py --input "./data/mic/MIC.csv" --output "./rot_generation/output/retrieval_Q_A_TARGET_rot/"

Run social-chem models

First, clone social-chemistry-101 into the root directory and follow instructions to prepare the GPT2-XL RoT model

Then run the following code

python -m rot_generation.generate_rots_decoder --model_directory "social-chemistry-101/output/gpt2-xl_rot_64_5epochs" --input "./data/mic/MIC.csv" --output "rot_generation/output_sc" --format_string "QA [rot] ~ rot" --gpu 3 --top_p 0.9

Compute all results

python -m rot_generation.metrics --input "rot_generation/output/*Q_A_TARGET_rot*" --output "all_results.csv"

4. Run Human Evaluation for RoT Generation

Prepare generations for human evaluation

python ./hit/Task_GeneratedRoTEvaluation/prepare_hit.py --mic "./data/mic/MIC.csv" --input_glob "./rot_generation/output/*Q_A_TARGET_rot*/test_generation*.csv" --output "./hit/Task_GeneratedRoTEvaluation/input/human_eval_25.csv" --nsamples 25

Set up the Qualification Test (after you modify the file with the proper API keys)

python ./hit/Task_GeneratedRoTEvaluation/qual_request.py

Run the GeneratedRoTEvaluation.html task on MTurk, using input from ./hit/Task_GeneratedRoTEvaluation/input and save file to ./hit/Task_GeneratedRoTEvaluation/output/human_eval.csv

Print inter-annotator agreements and evaluation results

python ./hit/Task_GeneratedRoTEvaluation/print_eval_results.py --input "./hit/Task_GeneratedRoTEvaluation/output/human_eval.csv"

5. Train RoT Attribute Classification Models

Generate backtranslations

1. Use different temperatures and different languages: German (de) and Russion (ru) Note: this will not work unless you first install fairseq according to these instructions

python attribute_clf/backtranslate_to_en.py --temperature 1.2 --k 5 --language "de" --target "rot" --input "data/mic/MIC.csv"
python attribute_clf/backtranslate_to_en.py --temperature 0.7 --k 5 --language "de" --target "rot" --input "data/mic/MIC.csv"
python attribute_clf/backtranslate_to_en.py --temperature 0.9 --k 5 --language "de" --target "rot" --input "data/mic/MIC.csv"
python attribute_clf/backtranslate_to_en.py --temperature 1.2 --k 5 --language "ru" --target "rot" --input "data/mic/MIC.csv"
python attribute_clf/backtranslate_to_en.py --temperature 0.7 --k 5 --language "ru" --target "rot" --input "data/mic/MIC.csv"
python attribute_clf/backtranslate_to_en.py --temperature 0.9 --k 5 --language "ru" --target "rot" --input "data/mic/MIC.csv"

2. Join backtranslations

python attribute_clf/join_backtranslations.py

Run For Hyperparameter Tunings

1. Run for all learning rates and epochs

CUDA_VISIBLE_DEVICES=0 bash attribute_clf/tuning_script_ordinal.sh 0 "violation-severity" "./data/mic/MIC.csv" "rot" "bert-base-uncased"
CUDA_VISIBLE_DEVICES=0 bash attribute_clf/tuning_script_ordinal.sh 0 "violation-severity" "./data/mic/MIC.csv" "rot" "albert-base-v2"

CUDA_VISIBLE_DEVICES=1 bash attribute_clf/tuning_script_ordinal.sh 1 "rot-agree-collapsed" "./data/mic/MIC.csv" "rot" "bert-base-uncased"
CUDA_VISIBLE_DEVICES=1 bash attribute_clf/tuning_script_ordinal.sh 1 "rot-agree-collapsed" "./data/mic/MIC.csv" "rot" "albert-base-v2"

CUDA_VISIBLE_DEVICES=2 bash attribute_clf/tuning_script_multilabel.sh 2 "moral-vector" "./data/mic/MIC.csv" "rot" "bert-base-uncased"
CUDA_VISIBLE_DEVICES=2 bash attribute_clf/tuning_script_multilabel.sh 2 "moral-vector" "./data/mic/MIC.csv" "rot" "albert-base-v2"


CUDA_VISIBLE_DEVICES=3 bash attribute_clf/tuning_script.sh 3 "A_agrees" "./data/mic/MIC.csv" "QA" "rot" "albert-base-v2"
CUDA_VISIBLE_DEVICES=3 bash attribute_clf/tuning_script.sh 3 "A_agrees" "./data/mic/MIC.csv" "QA" "rot" "bert-base-uncased"

2. Find and run the best model for each task (running scripts are just given as an example, but you should swap the hyperparameters to match the best model output from find_best_model.py)

Violation Severity

python attribute_clf/find_best_model.py --dir "attribute_clf/cross_validation_violation-severity/" --metric "MSE" --negate --model "bert-base"

python -m attribute_clf.clf_sentence_pairs_ordinal --batchsize 16 --epochs 2 --lr "5e-5" --gpu 0 --label "violation-severity" --input "./data/mic/MIC.csv" --output "attribute_clf/results_violation-severity" --sent1 "rot" --bert_model "bert-base-uncased" --test

Moral Vector

python attribute_clf/find_best_model.py --dir "attribute_clf/cross_validation_moral-vector/" --metric "f1_macro" --model "bert-base"

python -m attribute_clf.clf_sentence_pairs_multilabel --batchsize 16 --epochs 8 --lr "5e-5" --gpu 2 --label "moral-vector" --input "./data/mic/MIC.csv" --output "attribute_clf/results_moral-vector" --sent1 "rot" --label_classes 0 1 2 3 4 5 --bert_model "bert-base-uncased" --test

Answer Alignment

python attribute_clf/find_best_model.py --dir "attribute_clf/cross_validation_A_agrees/" --metric "f1_2" --model "bert-base"

python -m attribute_clf.clf_sentence_pairs --batchsize 8 --epochs 3 --lr "5e-5" --gpu 2 --label "A_agrees" --input "./data/mic/MIC.csv" --output "attribute_clf/results_A_agrees" --sent1 "QA" --sent2 "rot" --bert_model "albert-base-v2" --test

6. Run Human Benchmark for RoT Classification

Prepare labels for human classification

python ./hit/Task_SecondaryMoralEvaluation/prepare_human_benchmark.py --input "./data/mic/MIC.csv" --nsamples 300

7. Compute Inter-annotator agreement for Moral Evaluation

Prepare generations for human evaluation [here {input} is the name of a file from the primary MoralEvaluation task (e.g. filtered_clf_sufficient_moral_blenderbot_0_100_FINISHED.csv)]

python ./hit/Task_SecondaryMoralEvaluation/prepare_hit.py --input "{input}"

Set up the Qualification Test (after you modify the file with the proper API keys)

python ./hit/Task_SecondaryMoralEvaluation/qual_request.py

Run the SecondaryMoralEvaluation.html task on MTurk, using input from ./hit/Task_SecondaryMoralEvaluation/input and save files to ./hit/Task_SecondaryMoralEvaluation/output

Finally, create cleaned output file

python ./hit/Task_SecondaryMoralEvaluation/prepare_outfile.py --output "./hit/Task_SecondaryMoralEvaluation/secondary_moral_eval_oct23.csv"

8. Generate plots for the paper

Retrieve the political leanings of all workers, here replacing {qual_id} with the appropriate MTurk QualificationTypeId for the Qual_PoliticalLeaning task

python ./hit/Qual_PoliticalLeaning/lookup_worker_leanings.py --qual_id "{qual_id}"

Run everything in the jupyter notebook, dataset-statistics.ipynb