Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

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Deep Learningsccl
Overview

Supporting Clustering with Contrastive Learning

SCCL (NAACL 2021) Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ramesh Nallapati, Andrew Arnold, and Bing Xiang.

Requirements

Datasets:

In additional to the original data, SCCL requires a pair of augmented data for each instance. See our paper for details.

Dependencies:

python==3.6. 
pytorch==1.6.0. 
sentence-transformers==0.3.8. 
transformers==3.3.0. 
tensorboardX==2.1.  

To run the code:

1. put your dataset in the folder "./datasamples"  # for some license issue, we are not able to release the dataset now, we'll release the datasets asap
2. bash ./scripts/run.sh # you need change the dataset info and results path accordingly

Citation:

@inproceedings{zhang-etal-2021-supporting,
title = "Supporting Clustering with Contrastive Learning",
author = "Zhang, Dejiao  and Nan, Feng  and Wei, Xiaokai  and
  Li, Shang-Wen  and Zhu, Henghui  and McKeown, Kathleen  and
  Nallapati, Ramesh  and Arnold, Andrew O.  and Xiang, Bing",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.naacl-main.427",
pages = "5419--5430",
abstract = " ",}
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