Arturo Argueta — accepted AI-conference papers

2024

Towards A World-English Language Model for on-Device Virtual Assistants

Rricha Jalota, Lyan Verwimp, Markus Nußbaum-Thom, Amr El-Desoky Mousa, Arturo Argueta, Youssef Oualil

ICASSP 2024accepted

Neural Network Language Models (NNLMs) for Virtual Assistants (VAs) are generally language-, region-, and in some cases, device-dependent, which increases the effort to scale and maintain them. Combining NNLMs for one or more of the categories is one way to improve scalability. In this work, we comb…

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2023

Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices

Mingbin Xu, Congzheng Song, Ye Tian, Neha Agrawal, Filip Granqvist, Rogier C. van Dalen +7

ICASSP 2023accepted

Federated Learning (FL) is a technique to train models on distributed edge devices with local data samples. Differential Privacy (DP) can be applied with FL to provide a formal privacy guarantee for sensitive data on device. Our goal is to train a large neural network language model (NNLM) on comput…

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