Towards a World-English Language Model for On-Device Virtual Assistants
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.
6604de6ae2c63a55b0bdf8d8
Rricha Jalota
6604de6ae2c63a55b0bdf8d9
Lyan Verwimp
6604de6ae2c63a55b0bdf8da
Markus Nussbaum-Thom
6604de6ae2c63a55b0bdf8db
Amr Mousa
6604de6ae2c63a55b0bdf8dc
Arturo Argueta
6604de6ae2c63a55b0bdf8dd
Youssef Oualil
Towards a World-English Language Model for On-Device Virtual Assistants
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.
Adapter bottlenecks are used to integrate dialect-specific characteristics into a World English NNLM for virtual assistants, improving accuracy while maintaining low latency and memory usage.
Neural Network Language Models Virtual Assistants adapter bottlenecks World English multi-dialect dialect-specific latency memory constraints