Enhancing Natural Language Processing in Virtual Assistants through Transformer Models

Description

This study aims to investigate the possibility of improving Natural Language Processing (NLP) skills in virtual assistants by using Transformer models. Virtual assistants are becoming more common in various fields, including smart homes, IoT voice interactions, mobile data mining, and healthcare (Kang et al., 2020). However, existing NLP techniques employed in virtual assistants frequently need help interpreting user goals and fully handling complicated inquiries. Vaswani et al. (2017) unveiled the Transformer model that changed NLP tasks with its attention mechanism and parallel processing capabilities. This study looks at how adopting Transformer-based methodologies might improve the performance, efficiency, and user experience of virtual assistants in various applications.

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Research Question

How might the use of Transformer models improve Natural Language Processing in virtual assistants, and what are the possible applications and advantages of such an approach in diverse domains?

The research will begin by reviewing the current literature on NLP approaches used in virtual assistants, emphasizing the combined tasks of intent detection and slot filling, as emphasized in the paper by Ni et al. (2020). The survey done by Wang et al. (2021) on incorporating human intelligence into NLP loops will also be studied to grasp the more significant implications of NLP improvements. Furthermore, as recognized by Padmanaban et al. (2023), it is critical to investigate the issues encountered in human-computer interfaces for mobile data mining, as well as the developing applications of text analytics and NLP in healthcare, as addressed by Eisenstein (2019).

Furthermore, the research will look at the construction of ideal search engines combining text summarization techniques and artificial intelligence, as described by Sekaran et al. (2020), as well as Bharatiya’s (2023) complete assessment of deep learning approaches for NLP. The research will also investigate the function of artificial intelligence in enhancing human-computer interactions, particularly in computer systems, as written by Chowdhary & Chowdhary (2020), as well as the special issue on AI in Human-Computer Interaction given by Antona et al. (2023). Finally, as done by Bouraoui et al. (2022), a complete overview of deep learning approaches for NLP, including their applications, will be reviewed, as will the basics of deep learning in NLP, as given by Yang et al. (2019).

References

Antona, M., Margetis, G., Ntoa, S., & Degen, H. (2023). Special issue on AI in HCI. International Journal of Human-Computer Interaction, 39(9), 1723-1726. https://doi.org/10.1080/10447318.2023.2177421

Bharadiya, J. (2023). A comprehensive survey of deep learning techniques natural language processing. European Journal of Technology, 7(1), 58-66. https://doi.org/10.47672/ejt.1473

Bouraoui, A., Jamoussi, S., & Hamadou, A. B. (2022). A comprehensive review of deep learning for natural language processing. International Journal of Data Mining, Modelling and Management, 14(2), 149-182. https://doi.org/10.1504/IJDMMM.2022.123356