chiahsuan156 / DST-as-Prompting

Source code for Dialogue State Tracking with a Language Model using Schema-Driven Prompting

SDP-DST: DST-as-Prompting

This is the original implementation of "Dialogue State Tracking with a Language Model using Schema-Driven Prompting" by Chia-Hsuan Lee, Hao Cheng and Mari Ostendorf.

Installation | Preprocess | Training | Evaluation | | Citation

Installation

conda create -n DST-prompt python=3.7
cd DST-as-Prompting
conda env update -n DST-prompt -f env.yml

To use Hugggingface seq2seq training scripts, install from source.

pip install git+https://github.com/huggingface/transformers.git@2c2a31ffbcfe03339b1721348781aac4fc05bc5e

Pip install requirements to use Huggingface training script

cd transformers/examples/pytorch/summarization/
pip install -r requirements.txt

Download and Preprocess Data

Please download the data from MultiWOZ github.

cd ~/DST-as-Prompting
git clone https://github.com/budzianowski/multiwoz.git

$DATA_DIR will be multiwoz/data/MultiWOZ_2.2

cd ~/DST-as-Prompting
python preprocess.py $DATA_DIR

Training

cd transformers
python examples/pytorch/summarization/run_summarization.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_predict \
    --train_file "$DATA_DIR/train.json" \
    --validation_file "$DATA_DIR/dev.json" \
    --test_file "$DATA_DIR/test.json" \
    --source_prefix "" \
    --output_dir /tmp/t5small_mwoz2.2 \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --predict_with_generate \
    --text_column="dialogue" \
    --summary_column="state" \
    --save_steps=500000

• --model_name_or_path: name of the model card, like t5-small, t5-base, etc

This should take ~32 hours to train on a single GPU. At the end of training, the model will get predictions on $test_file and store the results at $output_dir/generated_predictions.txt .

Evaluation

cd ~/DST-as-Prompting

python postprocess.py --data_dir "$DATA_DIR" --out_dir "$DATA_DIR/dummy/" --test_idx "$DATA_DIR/test.idx" \
    --prediction_txt "$output_dir/generated_predictions.txt"

python eval.py --data_dir "$DATA_DIR" --prediction_dir "$DATA_DIR/dummy/" \
    --output_metric_file "$DATA_DIR/dummy/prediction_score"

Citation and Contact

If you find our code or paper useful, please cite the paper:

@inproceedings{lee2021dialogue,
  title={Dialogue State Tracking with a Language Model using Schema-Driven Prompting},
  author={Lee, Chia-Hsuan and Cheng, Hao and Ostendorf, Mari},
  booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  pages={4937--4949},
  year={2021}
}

Please contact Chia-Hsuan Lee (chiahlee[at]uw.edu) for questions and suggestions.