Entity Resolution Based on Pre-trained Language Models with Two Attentions

Entity Resolution (ER) is one of the most important issues for improving data quality, which aims to identify the records from one and more datasets that refer to the same real-world entity. For the textual datasets with the attribute values of long word sequences, the traditional methods of ER may fail to capture accurately the semantic information of records, leading to poor effectiveness. To address this challenging problem, in this paper, by using pre-trained language model RoBERTa and by fine-tuning it in the training process, we propose a novel entity resolution model IGaBERT, in which interactive attention is applied to capture token-level differences between records and to break the restriction that the schema required identically, and then global attention is utilized to determine the importance of these differences. Extensive experiments without injecting domain knowledge are conducted to measure the effectiveness of the IGaBERT model over both structured datasets and textual datasets. The results indicate that IGaBERT significantly outperforms several state-of-the-art approaches over textual datasets, especially with small size of training data, and it is highly competitive with those approaches over structured datasets.