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ProBERT: Product Data Classification with Fine-tuning BERT Model

Abstract. In this paper, we describe our submission to the semantic web challenge on mining the product data in websites (MWPD2020). The dataset provided 19K instances of product data collected from various websites. The task is to predict the category, defined as hierarchical taxonomy as provided in the training set, of the product titles in the test set. In our approach, we present a simple BERT-based model (dubbed ProBERT) for classifying product data into one or more categories. We trained our system on products titles and descriptions to learn semantic representation. The participated systems are evaluated using weighted-average precision, recall and F1-score.