Abstract
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.- Anthology ID:
- N18-3018
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans - Louisiana
- Editors:
- Srinivas Bangalore, Jennifer Chu-Carroll, Yunyao Li
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 145–152
- Language:
- URL:
- https://aclanthology.org/N18-3018
- DOI:
- 10.18653/v1/N18-3018
- Cite (ACL):
- Anuj Kumar Goyal, Angeliki Metallinou, and Spyros Matsoukas. 2018. Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 145–152, New Orleans - Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents (Goyal et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/N18-3018.pdf