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.
**************************** 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.
We release three pre-trained models: DialoFlow_base, DialoFlow_medium, and DialoFlow_large.
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.
cd dailydialog
bash fine-tune.shFlow 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.
Citation
Please cite our paper if you use DialoFlow in your work.
@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}
}