Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization

Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain. However, annotating data for each domain is both financially costly and non-scalable so we should fully utilize information across all domains. One existing a...

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Bibliographic Details
Published in:arXiv.org 2021-11
Main Authors: Qin, Libo, Ni, Minheng, Zhang, Yue, Che, Wanxiang, Li, Yangming, Liu, Ting
Format: Article
Language:English
Subjects:
Knowledge management
Mathematical models
Parameterization
Parameters
Supervised learning
Training
Online Access:Get full text
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Summary:Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain. However, annotating data for each domain is both financially costly and non-scalable so we should fully utilize information across all domains. One existing approach solves the problem by conducting multi-domain learning, using shared parameters for joint training across domains. We propose to improve the parameterization of this method by using domain-specific and task-specific model parameters to improve knowledge learning and transfer. Experiments on 5 domains show that our model is more effective for multi-domain SLU and obtain the best results. In addition, we show its transferability by outperforming the prior best model by 12.4\% when adapting to a new domain with little data.
ISSN:2331-8422