Multi-task student teacher based unsupervised domain adaptation for address parsing
In an e-commerce business, the ability to parse postal addresses into sub-component entities (such as building, locality) is essential to take automated actions at scale for successful delivery of shipments. The entities can be leveraged to build applications for logistics related operations, e.g. geocoding, assessing address completeness. Training an accurate address parser requires a significant number of manually labeled examples which is very expensive to create, especially when trying to build model(s) for multiple countries with unique address structure. To tackle this problem, in this paper, we present a novel Unsupervised Domain Adaptation (UDA) framework to transfer knowledge acquired by training a parser on labeled data from one country (source domain) to another (target domain) with unlabeled data. We specifically propose a multi-task student-teacher model comprising of three components: 1) specialized teachers trained on source data to create a pseudo labeled dataset, 2) consistency regularization, that uses a new data augmentation technique for sequence tagging data, and 3) boundary detection, leveraging signals in addresses like commas and text box boundaries. Multiple experiments on diverse address datasets demonstrate that our approach outperforms state-of-the-art UDA baselines for Named Entity Recognition (NER) task in terms of F1-score by 2-9%.