The AT&T spoken language understanding system
- 19 December 2005
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Audio, Speech, and Language Processing
- Vol. 14 (1), 213-222
- https://doi.org/10.1109/tsa.2005.854085
Abstract
Spoken language understanding (SLU) aims at extracting meaning from natural language speech. Over the past decade, a variety of practical goal-oriented spoken dialog systems have been built for limited domains. SLU in these systems ranges from understanding predetermined phrases through fixed grammars, extracting some predefined named entities, extracting users' intents for call classification, to combinations of users' intents and named entities. In this paper, we present the SLU system of VoiceTone/spl reg/ (a service provided by AT&T where AT&T develops, deploys and hosts spoken dialog applications for enterprise customers). The SLU system includes extracting both intents and the named entities from the users' utterances. For intent determination, we use statistical classifiers trained from labeled data, and for named entity extraction we use rule-based fixed grammars. The focus of our work is to exploit data and to use machine learning techniques to create scalable SLU systems which can be quickly deployed for new domains with minimal human intervention. These objectives are achieved by 1) using the predicate-argument representation of semantic content of an utterance; 2) extending statistical classifiers to seamlessly integrate hand crafted classification rules with the rules learned from data; and 3) developing an active learning framework to minimize the human labeling effort for quickly building the classifier models and adapting them to changes. We present an evaluation of this system using two deployed applications of VoiceTone/spl reg/.Keywords
This publication has 19 references indexed in Scilit:
- Boosting with prior knowledge for call classificationIEEE Transactions on Speech and Audio Processing, 2005
- Optimizing SVMs for complex call classificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Using predicate-argument structures for information extractionPublished by Association for Computational Linguistics (ACL) ,2003
- Automated natural spoken dialogComputer, 2002
- Stochastic language adaptation over time and state in natural spoken dialog systemsIEEE Transactions on Speech and Audio Processing, 2000
- A Tutorial on Support Vector Machines for Pattern RecognitionData Mining and Knowledge Discovery, 1998
- How may I help you?Speech Communication, 1997
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997
- PARADISEPublished by Association for Computational Linguistics (ACL) ,1997
- Evaluation of spoken language systemsPublished by Association for Computational Linguistics (ACL) ,1990