An Embedded Deep Learning based Word Prediction
arXiv:1707.01662
Abstract
Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation. However, deploying huge language models for mobile device such as on-device keyboards poses computation as a bottle-neck due to their puny computation capacities. In this work we propose an embedded deep learning based word prediction method that optimizes run-time memory and also provides a real time prediction environment. Our model size is 7.40MB and has average prediction time of 6.47 ms. We improve over the existing methods for word prediction in terms of key stroke savings and word prediction rate.
5 pages, 3 figures, EMNLP 2017 submitted
References in corpus (10)
- Adam: A Method for Stochastic Optimization
- Distilling the Knowledge in a Neural Network
- Neural Architecture Search with Reinforcement Learning
- Recurrent Neural Network Regularization
- Compressing Deep Convolutional Networks using Vector Quantization
- Visualizing and Understanding Recurrent Networks
- Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
- On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition
- Gated Word-Character Recurrent Language Model
- Learning Compact Recurrent Neural Networks