In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. In such cases, the common practice of fine-tuning pre-trained models, such as BERT, for a target classification task, is prone to produce poor performance. We suggest a method to boost the performance of such models by adding an intermediate unsupervised classification task, between the pre-training and fine-tuning phases. As such an intermediate task, we perform clustering and train the pre-trained model on predicting the cluster labels.We test this hypothesis on various data sets, and show that this additional classification phase can significantly improve performance, mainly for topical classification tasks, when the number of labeled instances available for fine-tuning is only a couple of dozen to a few hundred.
@inproceedings{DBLP:conf/acl/ShnarchGHDCAS22,
author = {Eyal Shnarch and
Ariel Gera and
Alon Halfon and
Lena Dankin and
Leshem Choshen and
Ranit Aharonov and
Noam Slonim},
editor = {Smaranda Muresan and
Preslav Nakov and
Aline Villavicencio},
title = {Cluster {\&} Tune: Boost Cold Start Performance in Text Classification},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational
Linguistics (Volume 1: Long Papers), {ACL} 2022, Dublin, Ireland,
May 22-27, 2022},
pages = {7639--7653},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://doi.org/10.18653/v1/2022.acl-long.526},
doi = {10.18653/V1/2022.ACL-LONG.526},
timestamp = {Mon, 01 Aug 2022 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/acl/ShnarchGHDCAS22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.