| # BERT-DST | |
| Contact: Guan-Lin Chao ([email protected]) | |
| Source code of our paper [BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer](https://arxiv.org/abs/1907.03040) (Interspeech 2019). | |
| ``` | |
| @inproceedings{chao2019bert, | |
| title={{BERT-DST}: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer}, | |
| author={Chao, Guan-Lin and Lane, Ian}, | |
| booktitle={INTERSPEECH}, | |
| year={2019} | |
| } | |
| ``` | |
| Tested on Python 3.6, Tensorflow==1.13.0rc0 | |
| ## Required packages (no need to install, just provide the paths in code): | |
| 1. [bert](https://github.com/google-research/bert) | |
| 2. uncased_L-12_H-768_A-12: pretrained [BERT-Base, Uncased] model checkpoint. Download link in [bert](https://github.com/google-research/bert). | |
| ## Datasets: | |
| [dstc2-clean](https://github.com/guanlinchao/bert-dst/blob/master/storage/dstc2-clean.zip), [woz_2.0](https://github.com/guanlinchao/bert-dst/blob/master/storage/woz_2.0.zip), [sim-M and sim-R](https://github.com/google-research-datasets/simulated-dialogue) |