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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2022,7,31]]},"abstract":"<jats:p>\n            Spoken language understanding (SLU) has been addressed as a supervised learning problem, where a set of training data is available for each domain. However, annotating data for a new domain can be both financially costly and non-scalable. One existing approach solves the problem by conducting multi-domain learning where parameters are shared for joint training across domains, which is\n            <jats:italic>domain-agnostic<\/jats:italic>\n            and\n            <jats:italic>task-agnostic<\/jats:italic>\n            . In the article, we propose to improve the parameterization of this method by using domain-specific and task-specific model parameters for fine-grained knowledge representation and transfer. Experiments on five domains show that our model is more effective for multi-domain SLU and obtain the best results. In addition, we show its transferability when adapting to a new domain with little data, outperforming the prior best model by 12.4%. Finally, we explore the strong pre-trained model in our framework and find that the contributions from our framework do not fully overlap with contextualized word representations (RoBERTa).\n          <\/jats:p>","DOI":"10.1145\/3502198","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T13:29:54Z","timestamp":1642685394000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-domain Spoken Language Understanding Using Domain- and Task-aware Parameterization"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3619-675X","authenticated-orcid":false,"given":"Libo","family":"Qin","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuxuan","family":"Wei","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minheng","family":"Ni","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Westlake University, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanxiang","family":"Che","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangming","family":"Li","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Liu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,1,20]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Snips voice platform: An embedded spoken language understanding system for private-by-design voice interfaces","author":"Coucke Alice","year":"2018","unstructured":"Alice Coucke, Alaa Saade, Adrien Ball, Th\u00e9odore Bluche, Alexandre Caulier, David Leroy, Cl\u00e9ment Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril et\u00a0al. 2018. 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