# Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents

arXiv: https://arxiv.org/abs/1805.01542 · HTML: https://mlsanity.com/paper/1805.01542/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

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

- [An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2110.13691/) (2021)
- [Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training](https://mlsanity.com/paper/2305.12793/) (2023)
- [Using Synthetic Data for Conversational Response Generation in Low-resource Settings](https://mlsanity.com/paper/2204.02653/) (2022)
- [Evaluating the Utility of GAN Generated Synthetic Tabular Data for Class Balancing and Low Resource Settings](https://mlsanity.com/paper/2306.13929/) (2023)

## Abstract (arXiv)

Fast expansion of natural language functionality of intelligent virtual agents is critical for achieving engaging and informative interactions. However, developing accurate models for new natural language domains is a time and data intensive process. We propose efficient deep neural network architectures that maximally re-use available resources through transfer learning. Our methods are applied for expanding the understanding capabilities of a popular commercial agent and are evaluated on hundreds of new domains, designed by internal or external developers. We demonstrate that our proposed methods significantly increase accuracy in low resource settings and enable rapid development of accurate models with less data.