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BERT-DST

The code has been tested with Python 3 and PyTorch 1.5.0. Note that the code in the folder pytorch_pretrained_bert was originally from the Hugging Face team. With minor modifications, you can use the latest version of huggingface/transformers.

Commands

An example training command (using BERT-Base) is: python main.py --do_train --data_dir=data/woz/ --bert_model=bert-base-uncased --output_dir=outputs

An example training command (using BERT-Large) is: python main.py --do_train --data_dir=data/woz/ --bert_model=bert-large-uncased --output_dir=outputs

Results

The table below shows the results on the WoZ restaurant reservation datasets.

Model Joint Goal (WoZ) Turn Request (WoZ)
Neural Belief Tracker - DNN 84.4% 91.2%
Neural Belief Tracker - CNN 84.2% 91.6%
GLAD 88.1 ± 0.4% 97.1 ± 0.2%
Simple BERT Model (BERT-Base) 90.5% 97.6%

Simple BERT Model

The figure below shows the architecture of the simple BERT Model.

Please cite our related paper A Simple but Effective BERT Model for Dialog State Tracking on Resource-Limited Systems if you find this useful.

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A Simple but Effective BERT Model for Dialog State Tracking on Resource-Limited Systems

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