Cluster & Tune: Boost Cold Start Performance in Text Classification
This addresses the cold start problem in text classification for practitioners dealing with limited labeled data, though it is incremental as it builds on existing fine-tuning methods.
The paper tackles the problem of poor performance in text classification during cold start scenarios with scarce labeled data by introducing an intermediate unsupervised clustering task between pre-training and fine-tuning, showing significant performance improvements, especially for topical classification tasks with only dozens to hundreds of labeled instances.
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.