Entity Matching with Transformer Architectures A Step Forward in Data Integration
Transformer architectures have proven to be very effective and provide state of the art results in many natural language tasks. The attention based architecture in combinationwith pre training on large amounts of text lead to the recent breakthrough and a variety of slightly different implementations. In this paper we analyze how well four of the most recent attention based transformer architectures (BERT[6] XLNet[33] RoBERTa[17] and DistilBERT [23] perform on the task of entity matching a crucial part of data integration. Entity matching (EM) is the task of finding data instances that refer to the same real world entity. It is a challenging task if the data instances consist of long textual data or if the data instances are "dirty" due to misplaced values. To evaluate the capability of transformer architectures and transfer learning on the task of EM, we empirically compare the four approaches on inherently difficult data sets. We show that transformer architectures outperform classical deep learning methods in EM[7, 20] by an average margin of 27.5%
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