# Continuous Learning for Large-scale Personalized Domain Classification

## TL;DR

This paper introduces CoNDA, a neural network approach for continuous, incremental domain classification in IPDAs, effectively handling new and personalized domains with high accuracy.

## Contribution

The paper presents CoNDA, a novel neural network method that enables dynamic, incremental learning of new personalized domains in large-scale IPDAs.

## Key findings

- CoNDA outperforms baseline methods in accuracy.
- It effectively incorporates new domains incrementally.
- High accuracy on both new and existing domains.

## Abstract

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.