Year: 2024
Type: article
Abstract: 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.
Authors: Van-Vinh Nguyen, Ha Nguyen-Tien, Anh-Quan Nguyen-Duc, Trung-Kien Vu, Cong Pham-Chi, Minh-Hieu Pham
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405087802