DAME: Domain Adaptation for Matching Entities

TL;DR

This paper introduces a domain adaptation approach for entity matching that leverages multiple source domains to improve performance on unseen target domains, especially in zero-shot and few-shot scenarios.

Contribution

It proposes a novel domain adaptation framework for entity matching that transfers knowledge from multiple source datasets to enhance generalization on new domains.

Findings

The method effectively transfers knowledge in zero-shot settings.

Fine-tuning improves performance over state-of-the-art methods.

The approach reduces overfitting to individual datasets.

Abstract

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.