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, 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.

@article{Nguyen2024,
  title     = {Recognizing Multi-Intent Commands of the Virtual Assistant with Low-Resource Languages},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Van-Vinh Nguyen and Ha Nguyen-Tien and Anh-Quan Nguyen-Duc and Trung-Kien Vu and Cong Pham-Chi and Minh-Hieu Pham},
  doi       = {10.14569/IJACSA.2024.01511125},
  url       = {https://doi.org/10.14569/IJACSA.2024.01511125}
}