Towards a World-English Language Model for On-Device Virtual Assistants
Rricha Jalota Lyan Verwimp Markus Nussbaum-Thom Amr Mousa Arturo Argueta Youssef Oualil
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.