Natural Language Processing (NLP) is a key enabler of conversational user interfaces for mobile chatbots and voice assistants, which are used more and more for smart applications such as customer support, personal assistance, and home automation. But deploying NLP models on mobile devices is challenging because these platforms are resource-constrained with limited processing power, memory, and battery life. In this paper, we discuss some of the most important NLP methods like tokenization, text categorization, and entity recognition, which are needed for mobile voice assistants and chatbots. We also discuss how there is a trade-off between local inference, where models are executed on the device, and cloud inference, which provides greater model capabilities at the cost of latency and privacy. Methods to improve NLP models for mobile devices, such as model compression, low-power designs, and hybrid solutions, are explored in great detail. Then, speech recognition integration with NLP in voice assistants is also explored with regard to challenges like real-time processing, privacy, and noise management. We conclude by defining future directions and challenges and highlighting the importance of scalable, energy-efficient, and privacy-preserving NLP systems for mobile devices.

NLP, mobile chatbots, voice assistants, tokenization, text classification, entity extraction, local inference, cloud-based models, model optimization, speech recognition

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