Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ictnlp/dialoflow
Code for ACL 2021 main conference paper "Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances".
https://github.com/ictnlp/dialoflow
dialogue-evaluation dialogue-generation dialogue-pretraining dialogue-systems flow-score
Last synced: about 2 months ago
JSON representation
Code for ACL 2021 main conference paper "Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances".
- Host: GitHub
- URL: https://github.com/ictnlp/dialoflow
- Owner: ictnlp
- License: mit
- Created: 2021-05-10T02:59:50.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-06-30T12:29:08.000Z (over 3 years ago)
- Last Synced: 2023-08-01T10:27:17.411Z (over 1 year ago)
- Topics: dialogue-evaluation, dialogue-generation, dialogue-pretraining, dialogue-systems, flow-score
- Language: Python
- Homepage:
- Size: 4.38 MB
- Stars: 91
- Watchers: 4
- Forks: 11
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances
This repository contains the code and pre-trained models for our ACL 2021 paper Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances [pdf](https://arxiv.org/abs/2106.02227).
**************************** **Updates** ****************************
The Chinese version is comming soon!
- 6/30: We released the code and pre-trained model (English version) of **DialoFlow**.
* 5/10: We released the code and pre-trained model of **Flow Score**. Try to use it!
## Overview
We propose the **DialoFlow**, a new paradigm to construct the dynamic information flow in the dialogue history by addressing the semantic influence brought about by each utterance. Besides, we design an automatic reference-free evaluation metric **Flow Score** based on the pre-trained DialoFlow for interactive dialogue quality evaluation.
## DialoFlow
### Requirements
torch==1.7.0
transformers==3.0.2
apex
### Pre-trained models
DialoFlow is pre-trained on the Reddit dataset based on the GPT-2.
For more details about the dataset, please refer to [DialoGPT](https://github.com/microsoft/DialoGPT).
We release three pre-trained models: [DialoFlow_base](https://drive.google.com/drive/folders/1yK__2CdD_4Ca3d02HkAph6ndkkgVR3YU?usp=sharing), [DialoFlow_medium](https://drive.google.com/drive/folders/12acVZVXu7dmeB-jBocEJSrMNytuai0CU?usp=sharing), and [DialoFlow_large](https://drive.google.com/drive/folders/11a2WZezOCvV652QSTYgZkAZ1nezXrhhi?usp=sharing).
Please download the pre-trained models under the path `models/`.
The fine-tuning models on the BST dataset and the Chinese version will be public soon.
### Dialogue Generation
We provide the code for dialogue generation using the pre-trained DialoFlow model.
The script `generate.py` contains two decoding methods: beam search and nucleus sampling.
You can modify the code for your own data and task.
### Fine-tuning
We fine-tuned the pre-trained model on the DailyDialog dataset.
```shell
cd dailydialog
bash fine-tune.sh
```
## Flow Score
**Flow Score** is an automatic reference-free evaluation metric for interactive dialogue evaluation based on the pre-trained DialoFlow. **Flow Score** can be found [here](https://github.com/ictnlp/DialoFlow/tree/main/FlowScore).
## Citation
Please cite our paper if you use DialoFlow in your work.
```bibtex
@inproceedings{li2021dialoflow,
title={Conversations are not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances},
author={Li, Zekang and Zhang, Jinchao and Fei, Zhengcong and Feng, Yang and Zhou, Jie},
booktitle={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year={2021}
}
```