Continuous Learning for Large-scale Personalized Domain Classification

Han Li, Jihwan Lee, Sidharth Mudgal, Ruhi Sarikaya, Young-Bum Kim

Abstract
Domain classification is the task to map spoken language utterances to one of the natural language understanding domains in intelligent personal digital assistants (IPDAs). This is observed in mainstream IPDAs in industry and third-party domains are developed to enhance the capability of the IPDAs. As more and more new domains are developed very frequently, how to continuously accommodate the new domains still remains challenging. Moreover, if one wants to use personalized information dynamically for better domain classification, it is infeasible to directly adopt existing continual learning approaches. In this paper, we propose CoNDA, a neural-based approach for continuous domain adaption with normalization and regularization. Unlike existing methods that often conduct full model parameter update, CoNDA only updates the necessary parameters in the model for the new domains. Empirical evaluation shows that CoNDA achieves high accuracy on both the accommodated new domains and the existing known domains for which input samples come with personal information, and outperforms the baselines by a large margin.
Anthology ID:
N19-1379
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3784–3794
Language:
URL:
https://aclanthology.org/N19-1379
DOI:
10.18653/v1/N19-1379
Bibkey:
Cite (ACL):
Han Li, Jihwan Lee, Sidharth Mudgal, Ruhi Sarikaya, and Young-Bum Kim. 2019. Continuous Learning for Large-scale Personalized Domain Classification. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3784–3794, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Continuous Learning for Large-scale Personalized Domain Classification (Li et al., NAACL 2019)
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PDF:
https://preview.aclanthology.org/naacl24-info/N19-1379.pdf
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