Sem-Dialogue
An implementation for paper "Semantic Representation for Dialogue Modeling". You may find our paper here.
Requirements
- python 3.6
- pytorch 1.6
- Tesla V100 (32G)
- Memory > 150G (for DialogRE)
We recommend to use conda to manage virtual environments:
conda create --name <env> --file requirements.txt
Data
The preprocessed data for DialogRE and DialogRG are avaliable at here and here, respectively.
Preprocessing
bash /path/to/code/preprocess.sh
Training
bash /path/to/code/run-dual(hier).sh
Evaluation
bash /path/to/code/eval.sh # for dialogue relation extraction
bash /path/to/code/decode.sh # for dialogue response generation
Pretrained Models
DialogRE
Data-v1
| Setting | dev-F1 | dev-F1c | test-F1 | test-F1c | checkpoint |
|---|---|---|---|---|---|
| Hier | 68.3 | 62.9 | 68.4 | 62.3 | model |
| Dual | 68.6 | 62.6 | 68.0 | 61.5 | model |
Data-v2
| Setting | dev-F1 | dev-F1c | test-F1 | test-F1c | checkpoint |
|---|---|---|---|---|---|
| Hier | 68.8 | 62.4 | 66.6 | 61.2 | model |
| Dual | 68.4 | 62.7 | 67.3 | 61.7 | model |
Todo
- clean code
References
@inproceedings{bai-etal-2021-semantic,
title = "Semantic Representation for Dialogue Modeling",
author = "Bai, Xuefeng and
Chen, Yulong and
Song, Linfeng and
Zhang, Yue",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.342",
doi = "10.18653/v1/2021.acl-long.342",
pages = "4430--4445"
}