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

• URL: http://arxiv.org/abs/2004.14871v2
• Date: Sun, 28 Nov 2021 13:57:51 GMT
• Title: Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization
• Authors: Libo Qin, Minheng Ni, Yue Zhang, Wanxiang Che, Yangming Li, Ting Liu
• Abstract summary: Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain. 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.
• Score: 78.93669377251396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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