This repository is the implementation of Dialogue State Tracking with Explicit Slot Connection Modeling.
Install requirements:
pip install -r requirements.txtOther preparations:
- Unzip
dataset/multiwoz.zip - Download character embedding and unzip
charNgram.txtto theembeddingfolder.
To train the model in the paper, run this command:
python run.py --dataset=2.0 --gpu=0,1,2 --batch_size=6 --gas=2To evaluate the model, specify your saved checkpoint file in train.py first and run:
python run.py --dataset=2.0 --is_test=True
Our model achieves the following performance on MultiWOZ 2.0 and 2.1 dataset:
| Model | MultiWOZ 2.0 | MultiWOZ 2.1 |
|---|---|---|
| DST-SC | 52.24% | 49.58% |
@inproceedings{ouyang-etal-2020-dialogue,
title = "Dialogue State Tracking with Explicit Slot Connection Modeling",
author = "Ouyang, Yawen and
Chen, Moxin and
Dai, Xinyu and
Zhao, Yinggong and
Huang, Shujian and
Chen, Jiajun",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.5",
doi = "10.18653/v1/2020.acl-main.5",
pages = "34--40",
abstract = "Recent proposed approaches have made promising progress in dialogue state tracking (DST). However, in multi-domain scenarios, ellipsis and reference are frequently adopted by users to express values that have been mentioned by slots from other domains. To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across different domains. Given a target slot, the slot connecting mechanism in DST-SC can infer its source slot and copy the source slot value directly, thus significantly reducing the difficulty of learning and reasoning. Experimental results verify the benefits of explicit slot connection modeling, and our model achieves state-of-the-art performance on MultiWOZ 2.0 and MultiWOZ 2.1 datasets.",
}