MaUde

Metric for automatic Unreferenced dialog evaluation.

Contains code of the paper titled "Learning an Unreferenced Metric for Online Dialogue Evaluation" to appear at ACL 2020, Arxiv

Installation

• pip install -r requirements.txt
• Install ParlAI

Getting the data

• Get the convai2 train and test data and pre-trained Distilbert embeddings here. Download and unzip in the folder convai2_data.
• Get the trained model checkpoints from here. Download and unzip into the folder full_acl_runs.
• For individual licensing reasons we cannot release the train/test data of MultiWoz, Frames and DailyDialog. Please send me a mail if you need them!
• Run inference using ./run_inference.sh

N.B. - For model names and checkpoints, please refer to run_inference.sh script.

Computing Backtranslation

We use FairSeq to compute back-translations. Our modified scripts are present in scripts folder, to run cd into that folder and run ./run_trans.sh.

Computing Corruption Files

In the data dump we already provide the corruption files used for training. To generate new corruption files on the dataset, use scripts/compute_corrupt.py.

Training Script

Uses Pytorch Lightning as the boilerplate for reproducibility.

python codes/trainer.py --mode train \
    --corrupt_type all \ 
    --batch_size 64 \
    --model_save_dir /checkpoint/koustuvs/dialog_metric/all_dailydialog_10_runs \
    --learn_down True --downsample True --down_dim 300 \
    --optim adam,lr=0.001 --dropout 0.2 --decoder_hidden 200 \ 
    --data_name convai2 \ 
    --data_loc /checkpoint/koustuvs/dialog_metric/convai2_data/ \
    --use_cluster

For baselines, add the appropriate flag:

--train_baseline [infersent/ruber/bertnli]

An example training script is provided at run_training.sh

Inference Script

# CUDA_VISIBLE_DEVICES=0 python codes/inference.py \ 
    --id $MODEL_ID --model_save_dir $MODEL_SAVE_DIR \
    --model_version $VERSION --train_mode nce \ 
    --corrupt_pre $DATA_LOC --test_suffix true_response \ 
    --test_column true_response --results_file "results.jsonl"

• Outputs the results in a jsonl file. To measure human correaltion with See et al 2019, specify --human_eval flag and --human_eval_file location.
• We have also added the script to run inference on our trained checkpoints - run_inference.sh.

Acknowledgements

• Pytorch Lightning - https://github.com/williamFalcon/pytorch-lightning
• HuggingFace Transformers - https://github.com/huggingface/transformers
• FairSeq - https://github.com/pytorch/fairseq
• Liming Vie's RUBER implementation - https://github.com/liming-vie/RUBER
• Pytorch 1.2.0 - https://pytorch.org/
• ParlAI - https://parl.ai/
• See et al 2019 data - https://parl.ai/projects/controllable_dialogue/

Questions

• Please send a mail to koustuv.sinha@mail.mcgill.ca for questions / clarifications.
• Open an Issue

Citation

If our work is useful for your research, consider citing it using the following bibtex:

@article{sinha2020maude,
  Author = {Koustuv Sinha and Prasanna Parthasarathi and Jasmine Wang and Ryan Lowe and William L. Hamilton and Joelle Pineau},
  Title = {Learning an Unreferenced Metric for Online Dialogue Evaluation},
  Year = {2020},
  journal = {ACL},
  arxiv = {2005.00583},
  url = {https://arxiv.org/abs/2005.00583}
}

License

This work is CC-BY-NC 4.0 (Attr Non-Commercial Inter.) licensed, as found in the LICENSE file.