Transformer-based Denoising Adversarial Variational Entity Resolution

Entity resolution (ER), precisely identifying different representations of the same real-world entities, is critical for data integration. The ER question has been studied for many years, and many methods have been proposed to solve it. Although deep learning has achieved good performance in ER tasks, there are some challenges regarding manual labeling and model transfer. This paper proposes a novel ER model, Transformer-based Denoising Adversarial Variational Entity Resolution (TdavER). For entity embedding, we develop an unsupervised entity embedding model based on denoising autoencoders and pre-trained language models, which takes corrupted input as training data to motivate the encoder to generate rather stable and robust high-quality entity representations. Furthermore, we propose an unsupervised entity feature transformation model based on adversarial variational autoencoders to ease the constraints on entity representations from training data. This transformation model converts low-level entity embeddings to high-level probability distributions, which are not constrained by the source data and contain deep similarity features. To better implement the feature transformation, we adopt adversarial networks to optimize the variational autoencoder’s training process and help it learn the correct posterior distribution. Extensive experiments confirms that the performance of our proposed TdavER is comparable with the current state-of-the-art ER methods and that its entity feature transformation model is transferable.