Abstract
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 (In this paper, we do not reveal the name of the e-commerce countries on which we evaluate our models due to business confidentiality. We also mask finer address details with (XX) to preserve customer’s privacy.) 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%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, W., Jiang, H., Wu, Q., Karlsson, B., Guan, Y.: AdvPicker: effectively leveraging unlabeled data via adversarial discriminator for cross-lingual NER. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, August 2021. https://doi.org/10.18653/v1/2021.acl-long.61. https://aclanthology.org/2021.acl-long.61
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2018). https://doi.org/10.48550/ARXIV.1810.04805. https://arxiv.org/abs/1810.04805
Han, X., Eisenstein, J.: Unsupervised domain adaptation of contextualized embeddings for sequence labeling (2019). https://doi.org/10.48550/ARXIV.1904.02817. https://arxiv.org/abs/1904.02817
Heinzerling, B., Strube, M.: BPEmb: tokenization-free pre-trained subword embeddings in 275 languages. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation, Miyazaki, Japan (2018)
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach (2019). https://doi.org/10.48550/ARXIV.1907.11692. https://arxiv.org/abs/1907.11692
openaddresses: Open addresses data (2019). https://openaddresses.io/
openstreetmap: Open street map data (2019). www.openstreetmap.org/#map=5/21.843/82.795
Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning (2016). https://doi.org/10.48550/ARXIV.1606.04586. https://arxiv.org/abs/1606.04586
Wu, Q., Lin, Z., Karlsson, B.F., Lou, J.G., Huang, B.: Single-/multi-source cross-lingual NER via teacher-student learning on unlabeled data in target language (2020). https://doi.org/10.48550/ARXIV.2004.12440. https://arxiv.org/abs/2004.12440
Yassine, M., Beauchemin, D.: Structured Multinational Address Data (2020). https://github.com/GRAAL-Research/deepparse-address-data
Yassine, M., Beauchemin, D., Laviolette, F., Lamontagne, L.: Leveraging subword embeddings for multinational address parsing. CoRR abs/2006.16152 (2020). https://arxiv.org/abs/2006.16152
Yassine, M., Beauchemin, D., Laviolette, F., Lamontagne, L.: Multinational address parsing: a zero-shot evaluation. CoRR abs/2112.04008 (2021). https://arxiv.org/abs/2112.04008
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sahay, R., Saladi, A., Sircar, P. (2023). Multi-task Student Teacher Based Unsupervised Domain Adaptation for Address Parsing. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13938. Springer, Cham. https://doi.org/10.1007/978-3-031-33383-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-33383-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33382-8
Online ISBN: 978-3-031-33383-5
eBook Packages: Computer ScienceComputer Science (R0)