Boosting classification reliability of NLP transformer models in the long run

• URL: http://arxiv.org/abs/2302.10016v1
• Date: Mon, 20 Feb 2023 14:46:54 GMT
• Title: Boosting classification reliability of NLP transformer models in the long run
• Authors: Zolt\'an Kmetty, Bence Koll\'anyi and Kriszti\'an Boros
• Abstract summary: This paper compares different approaches to fine-tune a BERT model for a long-running classification task. Our corpus contains over 8 million comments on COVID-19 vaccination in Hungary posted between September 2020 and December 2021.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Transformer-based machine learning models have become an essential tool for many natural language processing (NLP) tasks since the introduction of the method. A common objective of these projects is to classify text data. Classification models are often extended to a different topic and/or time period. In these situations, deciding how long a classification is suitable for and when it is worth re-training our model is difficult. This paper compares different approaches to fine-tune a BERT model for a long-running classification task. We use data from different periods to fine-tune our original BERT model, and we also measure how a second round of annotation could boost the classification quality. Our corpus contains over 8 million comments on COVID-19 vaccination in Hungary posted between September 2020 and December 2021. Our results show that the best solution is using all available unlabeled comments to fine-tune a model. It is not advisable to focus only on comments containing words that our model has not encountered before; a more efficient solution is randomly sample comments from the new period. Fine-tuning does not prevent the model from losing performance but merely slows it down. In a rapidly changing linguistic environment, it is not possible to maintain model performance without regularly annotating new text.

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