Скачать или смотреть Low-resource Deep Entity Resolution with Transfer and Active Learning

By: Jungo Kasai, University of Washington
Sept 9, 2019

Presented at ACL 2019: https://arxiv.org/abs/1906.08042

Abstract:
Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases. It is a key step for knowledge base creation and text mining. Recent adaptation of deep learning methods for ER mitigates the need for dataset-specific feature engineering by constructing distributed representations of entity records. While these methods achieve state-of-the-art performance over benchmark data, they require large amounts of labeled data, which are typically unavailable in realistic ER applications. In this paper, we develop a deep learning-based method that targets low-resource settings for ER through a novel combination of transfer learning and active learning. We design an architecture that allows us to learn a transferable model from a high-resource setting to a low-resource one. To further adapt to the target dataset, we incorporate active learning that carefully selects a few informative examples to fine-tune the transferred model. Empirical evaluation demonstrates that our method achieves comparable, if not better, performance compared to state-of-the-art learning-based methods while using an order of magnitude fewer labels.

Speaker bio:
Jungo Kasai is a second-year PhD student at the Paul G. Allen School of Computer Science & Engineering of the University of Washington, Seattle, advised by Noah A. Smith. He works on natural language processing and machine learning. His papers have been accepted to conferences, such as ACL, NAACL, and EMNLP. His research interests include representation learning for multilingual natural language processing, structured predictions, and syntactic parsing.