DAME: Domain Adaptation for Matching Entities
This addresses the need for reduced labeled data in entity matching across domains, though it is incremental as it builds on existing domain adaptation techniques.
The paper tackles the problem of entity matching requiring large labeled datasets per domain by proposing a domain adaptation method that transfers knowledge from multiple source domains to a target domain, achieving better generalization than state-of-the-art methods in zero-shot and fine-tuning scenarios.
Entity matching (EM) identifies data records that refer to the same real-world entity. Despite the effort in the past years to improve the performance in EM, the existing methods still require a huge amount of labeled data in each domain during the training phase. These methods treat each domain individually, and capture the specific signals for each dataset in EM, and this leads to overfitting on just one dataset. The knowledge that is learned from one dataset is not utilized to better understand the EM task in order to make predictions on the unseen datasets with fewer labeled samples. In this paper, we propose a new domain adaptation-based method that transfers the task knowledge from multiple source domains to a target domain. Our method presents a new setting for EM where the objective is to capture the task-specific knowledge from pretraining our model using multiple source domains, then testing our model on a target domain. We study the zero-shot learning case on the target domain, and demonstrate that our method learns the EM task and transfers knowledge to the target domain. We extensively study fine-tuning our model on the target dataset from multiple domains, and demonstrate that our model generalizes better than state-of-the-art methods in EM.