Updates: We release the T5 models as anonymity period is over.
Paper link: https://arxiv.org/abs/2203.01552
@article{shin2022dialogue,
title={Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking},
author={Shin, Jamin and Yu, Hangyeol and Moon, Hyeongdon and Madotto, Andrea and Park, Juneyoung},
journal={arXiv preprint arXiv:2203.01552},
year={2022}
}
- Installing the directory as pip will resolve all path issues
pip install -e .
pip install -r requirements.txt # requires python 3.8- Get MWOZ data (for 2.0 change all 2.1 to 2.0)
For 2.0
python scripts/create_data_mwoz.py --mwz_ver=2.0 --main_dir=data_mwoz_2.0 --target_path=data_mwoz_2.0/mwzFor 2.1
python scripts/create_data_mwoz.py --mwz_ver=2.1 --main_dir=data_mwoz_2.1 --target_path=data_mwoz_2.1/mwz- Training and Inference - Cross-domain
Pre-training
Example using T5 on Cross-domain pre-training. Note that this code will not work yet because we did not release our pretrained model checkpoints yet due to anonymity issues. We will release the checkpoints upon de-anonymization of the paper. Hence, we recommend using the following options to check our code.
We have uploaded the T5 pre-trained on Dialogue Summarization model on HuggingFace Model Hub at https://huggingface.co/jaynlp/t5-large-samsum. Now you can choose between BART and T5 as such:
model_name=bartandmodel_checkpoint=Salesforce/bart-large-xsum-samsummodel_name=t5andmodel_checkpoint=jaynlp/t5-large-samsum
CUDA_VISIBLE_DEVICES={gpu} python ds2/scripts/train_ds2.py \
--dev_batch_size=8 \
--test_batch_size=8 \
--train_batch_size=2 \
--n_epochs=100 \
--num_beams=1 \
--test_num_beams=1 \
--val_check_interval=1.0 \
--fewshot=0.01 \
--grad_acc_steps=1 \
--model_name=bart \
--model_checkpoint=Salesforce/bart-large-xsum-samsum \
--except_domain=attraction \
--mode=finetune \
--exp_name=bart-CD-1-Attr-pre \
--seed=577 \
--version=2.1Fine-tuning
CUDA_VISIBLE_DEVICES={gpu} python ds2/scripts/train_ds2.py \
--dev_batch_size=8 \
--test_batch_size=8 \
--train_batch_size=2 \
--n_epochs=100 \
--num_beams=1 \
--test_num_beams=1 \
--val_check_interval=1.0 \
--fewshot=0.01 \
--grad_acc_steps=1 \
--model_name=bart \
--model_checkpoint=Salesforce/bart-large-xsum-samsum \
--only_domain=attraction \
--mode=finetune \
--load_pretrained={bart-CD-1-Attr-pre/ckpt_path} \
--exp_name=bart-CD-1-Attr \
--seed=577 \
--version=2.1- Training and Inference - Multi-domain
CUDA_VISIBLE_DEVICES={gpu} python ds2/scripts/train_ds2.py \
--dev_batch_size=8 \
--test_batch_size=8 \
--train_batch_size=2 \
--n_epochs=100 \
--num_beams=1 \
--test_num_beams=1 \
--val_check_interval=1.0 \
--fewshot=0.01 \
--grad_acc_steps=1 \
--model_name=bart \
--model_checkpoint=Salesforce/bart-large-xsum-samsum \
--mode=finetune \
--exp_name=bart-MD-1 \
--seed=577 \
--version=2.1- Training and Inference - Cross-task
CUDA_VISIBLE_DEVICES={gpu} python ds2/scripts/train_ds2.py \
--dev_batch_size=8 \
--test_batch_size=8 \
--train_batch_size=2 \
--n_epochs=100 \
--num_beams=1 \
--test_num_beams=1 \
--val_check_interval=1.0 \
--fewshot=0.01 \
--grad_acc_steps=1 \
--model_name=bart \
--model_checkpoint=Salesforce/bart-large-xsum-samsum \
--mode=finetune \
--only_domain=attraction \
--exp_name=bart-CT-Attr-1 \
--seed=577 \
--version=2.1
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