Recognizing Multi-Intent Commands of the Virtual Assistant with Low-Resource Languages

Virtual Assistants (VAs) are widely used in many fields. Recently, VAs have been effectively applied in technical drawing tasks, such as in Photoshop and Microsoft Word. Understanding multi-intent commands in VAs poses a significant challenge, especially when the language in query is low-resource, l...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2024-01, Vol.15 (11)
Main Authors: Nguyen, Van-Vinh, Nguyen-Tien, Ha, Nguyen-Duc, Anh-Quan, Vu, Trung-Kien, Pham-Chi, Cong, Pham, Minh-Hieu
Format: Article
Language:English
Subjects:
Artificial intelligence
Automatic control
Automation
Cellular telephones
Commands
Computer science
Datasets
Design
Designers
Engineering drawings
Ethics
Grammar
Informatics
Language
Large language models
Natural language
Query languages
Smart houses
Supervisory control and data acquisition
Task complexity
User experience
Voice recognition
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Summary:Virtual Assistants (VAs) are widely used in many fields. Recently, VAs have been effectively applied in technical drawing tasks, such as in Photoshop and Microsoft Word. Understanding multi-intent commands in VAs poses a significant challenge, especially when the language in query is low-resource, like Vietnamese (no training dataset available for technical drawing domain), which features complex grammar and a limited domain of usage. In this work, we proposing a three-step process to develop a voice assistant capable of understanding multi-intent commands in VAs for low-resource languages, particularly in responding to the SCADA Framework (SF) for performing drawing tasks: (1) for the training dataset, we developed a semi-automatic method for building a labeled command corpus; applying this method to Vietnamese, we built a corpus that includes 3,240 labeled commands; (2) for the multi-intent command processing phase, we introduced a method for splitting multi-intent commands into single-intent commands to enable VAs to perform them more efficiently. By experimenting with the proposed method in Vietnamese, we developed a VA that supports drawing on SF with an accuracy of over 96%. With the results of this study, we can completely apply them to SCADA system products to support the automatic control of techinical drawing operations in them as VAs.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.01511125