A transformer-based architecture neural network approach to email message autocomplete

Abstract

This research endeavors to develop and investigate an innovative neural network architecture based on transformers, aiming to enhance the automatic completion of email messages. The study outlines the comprehensive process of architecture creation, training, and evaluation, while exploring the impact of diverse hyperparameters and layers. The central focus lies in crafting a transformer model proficient in capturing intricate long-range dependencies inherent in email communications. Prior to training, an extensive data cleansing procedure is executed. Given hardware resource constraints, the architecture undergoes preliminary training on a substantial corpus of publicly available web texts, followed by rigorous evaluation on an independent test dataset. Subsequent fine-tuning is performed on individualized user email data, accompanied by a thorough analysis of hyperparameter effects on the performance of the fine-tuned model. This analysis encompasses a comparative assessment of performance metrics both before and after the fine-tuning process. Through these objectives, this study aspires to study the construction of the architecture mentioned in relation to the email autocompletion mechanisms, taking advantage of the resources of neural networks based on transformers. In this way, an understanding of the applicability of transformer models in the context of email message generation is provided, allowing for the identification of limitations, areas of improvement, parameter tuning, and the development of a solid foundation for the code-level implementation of this technology in email communication applications.