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Towards a World-English Language Model for On-Device Virtual Assistants

Citation Author(s):
Rricha Jalota, Lyan Verwimp, Markus Nussbaum-Thom, Amr Mousa, Arturo Argueta, Youssef Oualil
Submitted by:
Rricha Jalota
Last updated:
16 April 2024 - 12:01am
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Document Year:
Rricha Jalota
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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 combine regional variants of English to build a ``World English'' NNLM for on-device VAs. In particular, we investigate the application of adapter bottlenecks to model dialect-specific characteristics in our existing production NNLMs and enhance the multi-dialect baselines. We find that adapter modules are more effective in modeling dialects than specializing entire sub-networks.
Based on this insight and leveraging the design of our production models, we introduce a new architecture for World English NNLM that meets the accuracy, latency and memory constraints of our single-dialect models.

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