This code is the official pytorch implementation of Efficient Dialogue State Tracking by Selectively Overwriting Memory.
Sungdong Kim, Sohee Yang, Gyuwan Kim, Sang-woo Lee
In ACL 2020.
python3.6
pytorch-transformers==1.0.0
torch==1.3.0a0+24ae9b5
wget==3.2
To download the MultiWOZ dataset and preprocess it, please run this script first.
You can choose the version of the dataset. ('2.1', '2.0')
The downloaded original dataset will be located in $DOWNLOAD_PATH, and after preprocessing, it will be located in $TARGET_PATH.
python3 create_data.py --main_dir $DOWNLOAD_PATH --target_path $TARGET_PATH --mwz_ver '2.1' # or '2.0'
To train the SOM-DST model, please run this script.
$DATASET_DIR is the root directory of the preprocessed dataset, and $SAVE_DIR is output directory that best_model's checkpoint will be saved.
This script contains the downloading process of pretrained-BERT checkpoint depending on --bert_ckpt_path. --bert_ckpt_path should contain either base or large.
python3 train.py --data_root $DATASET_DIR --save_dir $SAVE_DIR --bert_ckpt_path `bert-base-uncased-pytorch_model.bin --op_code '4'`
You can choose the operation set from various options via --op_code. The default setting is '4'.
OP_SET = {
'2': {'update': 0, 'carryover': 1},
'3-1': {'update': 0, 'carryover': 1, 'dontcare': 2},
'3-2': {'update': 0, 'carryover': 1, 'delete': 2},
'4': {'delete': 0, 'update': 1, 'dontcare': 2, 'carryover': 3},
'6': {'delete': 0, 'update': 1, 'dontcare': 2, 'carryover': 3, 'yes': 4, 'no': 5}
}If you want to evaluate the already trained model, you can run this script.
$MODEL_PATH is the checkpoint of the model used for evaluation, and $DATASET_DIR is the root directory of the preprocessed dataset.
You can download the pretrained SOM-DST model from here.
python3 evaluation.py --model_ckpt_path $MODEL_PATH --data_root $DATASET_DIR
--gt_op: give the ground-truth operation for the evaluation.
--gt_p_state: give the ground-truth previous dialogue state for the evaluation.
--gt_gen: give the ground-truth generation for the evaluation.
--eval_all: evaluate all combinations of these.
------------------------------
op_code: 4, is_gt_op: False, is_gt_p_state: False, is_gt_gen: False
Epoch 0 joint accuracy : 0.5309446254071661
Epoch 0 slot turn accuracy : 0.9736020629749801
Epoch 0 slot turn F1: 0.9171910941855902
Epoch 0 op accuracy : 0.9741675714802285
Epoch 0 op F1 : {'delete': 0.030501089324618733, 'update': 0.7959107608753526, 'dontcare': 0.3029556650246305, 'carryover': 0.9864975897034028}
Epoch 0 op hit count : {'delete': 21, 'update': 7474, 'dontcare': 123, 'carryover': 207712}
Epoch 0 op all count : {'delete': 1341, 'update': 10745, 'dontcare': 624, 'carryover': 208330}
Final Joint Accuracy : 0.37037037037037035
Final slot turn F1 : 0.9108339131790647
Latency Per Prediction : 24.461546 ms
-----------------------------
hotel 0.5154559505409583 0.973776403915519
train 0.7171922685656155 0.9874646772917451
restaurant 0.6596113809854268 0.9857737682165246
attraction 0.66872174270448 0.9873270311001585
taxi 0.5903426791277259 0.9803219106957396
| Model | MultiWOZ 2.0 | MultWOZ 2.1 |
|---|---|---|
| SOM-DST Base | 51.72 | 53.01 |
| SOM-DST Large | 52.32 | 53.68 |
@inproceedings{kim2020somdst,
title={Efficient Dialogue State Tracking by Selectively Overwriting Memory},
author={Kim, Sungdong and Yang, Sohee and Kim, Gyuwan and Lee, Sang-woo},
booktitle={ACL},
year={2020}
}If you have any questions, feel free to contact Sungdong Kim (sungdong.kim@navercorp.com) and Sohee Yang (sh.yang@navercorp.com)
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