{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cluster-tune-boost-cold-start-performance-in","title":"Cluster & Tune: Boost Cold Start Performance in Text Classification","arxiv_id":"2203.10581","date":"2022-03-20","proceeding":"ACL 2022 5","authors":["Eyal Shnarch","Ariel Gera","Alon Halfon","Lena Dankin","Leshem Choshen","Ranit Aharonov","Noam Slonim"],"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. 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