# Continuous Learning for Large-scale Personalized Domain Classification

arXiv: https://arxiv.org/abs/1905.00921 · HTML: https://mlsanity.com/paper/1905.00921/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2019.

## Built on by (most cited first, 4 of 4)

- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [TWEAC: Transformer with Extendable QA Agent Classifiers](https://mlsanity.com/paper/2104.07081/) (2021)
- [Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding](https://mlsanity.com/paper/2201.01420/) (2022)
- [Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation](https://mlsanity.com/paper/2202.13363/) (2022)

## Abstract (arXiv)

Domain classification is the task of mapping spoken language utterances to one of the natural language understanding domains in intelligent personal digital assistants (IPDAs). This is a major component in mainstream IPDAs in industry. Apart from official domains, thousands of third-party domains are also created by external developers to enhance the capability of IPDAs. As more domains are developed rapidly, the question of how to continuously accommodate the new domains still remains challenging. Moreover, existing continual learning approaches do not address the problem of incorporating personalized information dynamically for better domain classification. In this paper, we propose CoNDA, a neural network based approach for domain classification that supports incremental learning of new classes. Empirical evaluation shows that CoNDA achieves high accuracy and outperforms baselines by a large margin on both incrementally added new domains and existing domains.