title: Towards a World-English Language Model for On-Device Virtual Assistants
summary: 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.
paper_authors._id: 6604de6ae2c63a55b0bdf8d8
paper_authors.name: Rricha Jalota
paper_authors._id: 6604de6ae2c63a55b0bdf8d9
paper_authors.name: Lyan Verwimp
paper_authors._id: 6604de6ae2c63a55b0bdf8da
paper_authors.name: Markus Nussbaum-Thom
paper_authors._id: 6604de6ae2c63a55b0bdf8db
paper_authors.name: Amr Mousa
paper_authors._id: 6604de6ae2c63a55b0bdf8dc
paper_authors.name: Arturo Argueta
paper_authors._id: 6604de6ae2c63a55b0bdf8dd
paper_authors.name: Youssef Oualil
paper_title: Towards a World-English Language Model for On-Device Virtual Assistants
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Neural Network Language Models Virtual Assistants adapter bottlenecks World English multi-dialect dialect-specific latency memory constraints