Cluster & Tune: Boost Cold Start Performance in Text Classification

• URL: http://arxiv.org/abs/2203.10581v1
• Date: Sun, 20 Mar 2022 15:29:34 GMT
• Title: Cluster & Tune: Boost Cold Start Performance in Text Classification
• Authors: Eyal Shnarch, Ariel Gera, Alon Halfon, Lena Dankin, Leshem Choshen, Ranit Aharonov, Noam Slonim
• Abstract summary: In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. We suggest a method to boost the performance of such models by adding an intermediate unsupervised classification task.
• Score: 21.957605438780224
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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