@inproceedings{chen2023alleviating,
author = {Chen, Nuo and Shou (寿林钧), Linjun and Gong (YIMING), Ming and Pei, Jian and Cao, Bowen and Chang, Jianhui and Jiang (姜大昕), Daxin and Li, Jia},
title = {Alleviating Over-smoothing for Unsupervised Sentence Representation},
booktitle = {ACL 2023},
year = {2023},
month = {May},
abstract = {Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language models (PLMs) and contrastive learning have achieved promising results on this task. Experimentally, we observe that the over-smoothing problem reduces the capacity of these powerful PLMs, leading to sub-optimal sentence representations. In this paper, we present a Simple method named Self-Contrastive Learning (SSCL) to alleviate this issue, which samples negatives from PLMs intermediate layers, improving the quality of the sentence representation. Our proposed method is quite simple and can be easily extended to various state-of-the-art models for performance boosting, which can be seen as a plug-and-play contrastive framework for learning unsupervised sentence representation. Extensive results prove that SSCL brings the superior performance improvements of different strong baselines (e.g., BERT and SimCSE) on Semantic Textual Similarity and Transfer datasets. Our codes are available at this https URL.},
url = {http://approjects.co.za/?big=en-us/research/publication/alleviating-over-smoothing-for-unsupervised-sentence-representation/},
}