On-Device Neural Language Model Based Word Prediction

By Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim. 2018

32 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Rajiv Mathews
2. Jimmy J. Lin
3. Franccoise Beaufays
4. Andrew Straiton Hard
5. Mark Anderson
6. Kanishka Rao
7. Hubert Eichner
8. Sean Augenstein

Citations in the corpus

• Federated Learning for Mobile Keyboard Prediction (2018, methodology): “RNN language models optimized for word prediction rate and keystroke savings within inference-time latency and memory constraints have also been published [14, 15].”
• Distilling Neural Networks for Greener and Faster Dependency Parsing (2020, methodology): “Teacherstudent distillation has successfully been exploited in NLP for machine translation, language modelling, and speech recognition (Kim and Rush, 4 2016; Yu et al., 2018; Lu et al., 2017).”
• Distilling Knowledge for Fast Retrieval-based Chat-bots (2020, unknown): “Knowledge distillation has been successfully applied in natural language understanding, machine translation, and language modeling tasks [7, 16, 20].”
• Distilling Task-Specific Knowledge from BERT into Simple Neural Networks (2019, methodology): “In the NLP literature, it has previously been used in neural machine translation (Kim and Rush, 2016) and language modeling (Yu et al., 2018).”