DAMO-ConvAI/ssll

SSLL

Introduction

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SSLL (Semi-Supervised Lifelong Language Learning) is build by Conversational AI Team, Alibaba DAMO Academy.

The corresponding paper has been published at EMNLP 2022 Findings: "Semi-Supervised Lifelong Language Learning".

SSLL Implementation

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Requirements

pip install -r requirements.txt

Dataset

The datasets used in the experiments follows LAMOL.

Model Training and Evaluation

sh scripts/lltrain.sh 

The files required for training are under the folder unifymodel.

Citation

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If you use our code or find SSLL useful for your work, please cite our paper as:
@inproceedings{zhao2022semi,
title={Semi-Supervised Lifelong Language Learning}, author={Zhao, Yingxiu and Zheng, Yinhe and Yu, Bowen and Tian, Zhiliang and Lee, Dongkyu and Sun, Jian and Li, Yongbin and Zhang, Nevin L.}, booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Findings}, year={2022}
}

LAMOL citation:
@inproceedings{sun2019lamol,
title={LAMOL: LAnguage MOdeling for Lifelong Language Learning}, author={Sun, Fan-Keng and Ho, Cheng-Hao and Lee, Hung-Yi}, booktitle={International Conference on Learning Representations}, year={2020}
}