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Slot-Utterance Matching for Universal and Scalable Belief Tracking

This is the original PyTorch implemenation of SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking, Hwaran Lee*, Jinsik Lee*, and Tae-Yoon Kim, ACL 2019 (Short)

Requirements

• python 3.6
• pytorch >= 1.0
• Install python packages:
  • pip install -r requirements.txt

Usages

Data prepration & pre-procesisng

• Download corpus
  • WOZ2.0: download
  • MultiWOZ: download
    • Note: our experiments conducted on MultiWOZ 2.0 corpus
• Pre-process corpus
  • The download original corpus are loacated in data/$corpus/original
  • See data/$corpus/original/convert_to_glue_format.py
  • The pre-processed data are located in data/$corpus/

Train SUMBT

Please see run.sh

• Training and evaluation

python3 code/Main-multislot.py --do_train --do_eval --data_dir data/woz --bert_model bert-base-uncased --do_lower_case --task_name bert-gru-slot_query_multi --nbt rnn --output_dir exp-woz/model --target_slot all 

• Specifying slots (or domains) to train with the option --target_slots=$target_slots

  • e.g., For WOZ2.0, "0:1", "0:2", "1:2" (0=Area, 1=Food, 2=Pricerange)
  • e.g., For MultiWOZ, specify the domain name you want to exclude: "train" or "hotel"
  • If you want train with all slots, then --target_slots=all

• This code supports Multi-gpu training

  • CUDA_VISIBLE_DEVICES=$cuda python3 code/Main-multislot.py

Experiment results on MultiWOZ

• Command

python3 code/main-multislot.py --do_train --do_eval --num_train_epochs 300 --data_dir data/multiwoz --bert_model bert-base-uncased --do_lower_case --task_name bert-gru-sumbt --nbt rnn --output_dir exp-multiwoz/model --target_slot all --warmup_proportion 0.1 --learning_rate 1e-4 --train_batch_size 3 --eval_batch_size 16 --distance_metric euclidean --patience 15 --tf_dir tensorboard --hidden_dim 300 --max_label_length 32 --max_seq_length 64 --max_turn_length 22

• Experiment result

Notes and Acknowledgements

The code is developed based on PyTorch BERT from https://github.com/huggingface/pytorch-pretrained-BERT and The Annotated Transformer

Citation

@inproceedings{lee2019sumbt,
  title={SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking},
  author={Lee, Hwaran and Lee, Jinsik and Kim, Tae-Yoon},
  booktitle={Proceedings of the 57th Conference of the Association for Computational Linguistics},
  pages={5478--5483},
  year={2019}
}

Contact Information

Contact: Hwaran Lee (hwaran.lee@gmail.com), Jinsik Lee (jinsik16.lee@sktbrain.com), Tae-Yoon Kim (oceanos@sktbrain.com)