A Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents
Ryan Price, Mahnoosh Mehrabani, Narendra Gupta, Yeon-Jun Kim, Shahab Jalalvand, Minhua Chen, Yanjie Zhao, Srinivas Bangalore
Abstract
Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents. In enterprise virtual agents (EVAs), language understanding is substantially challenging. First, the users are infrequent callers who are unfamiliar with the expectations of a pre-designed conversation flow. Second, the users are paying customers of an enterprise who demand a reliable, consistent and efficient user experience when resolving their issues. In this work, we describe a general and robust framework for intent and entity extraction utilizing a hybrid of statistical and rule-based approaches. Our framework includes confidence modeling that incorporates information from all components in the SLU pipeline, a critical addition for EVAs to en- sure accuracy. Our focus is on creating accurate and scalable SLU that can be deployed rapidly for a large class of EVA applications with little need for human intervention.
BibTeX
@inproceedings{price-etal-2021-hybrid,
title = "A Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents",
author = "Price, Ryan and
Mehrabani, Mahnoosh and
Gupta, Narendra and
Kim, Yeon-Jun and
Jalalvand, Shahab and
Chen, Minhua and
Zhao, Yanjie and
Bangalore, Srinivas",
editor = "Kim, Young-bum and
Li, Yunyao and
Rambow, Owen",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-industry.9/",
doi = "10.18653/v1/2021.naacl-industry.9",
pages = "63--71"
}