An Embedded Deep Learning based Word Prediction

07/06/2017
by   Seunghak Yu, et al.
0

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.

READ FULL TEXT
research
05/14/2019

Is Word Segmentation Necessary for Deep Learning of Chinese Representations?

Segmenting a chunk of text into words is usually the first step of proce...
research
01/27/2020

The Final Frontier: Deep Learning in Space

Machine learning, particularly deep learning, is being increasing utilis...
research
05/18/2023

Deep Learning Methods for Extracting Metaphorical Names of Flowers and Plants

The domain of Botany is rich with metaphorical terms. Those terms play a...
research
07/27/2020

Next word prediction based on the N-gram model for Kurdish Sorani and Kurmanji

Next word prediction is an input technology that simplifies the process ...
research
10/31/2016

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

Recurrent neural networks (RNNs) have achieved state-of-the-art performa...
research
11/23/2022

Word-Level Representation From Bytes For Language Modeling

Modern language models mostly take sub-words as input, a design that bal...
research
09/13/2021

Graph Algorithms for Multiparallel Word Alignment

With the advent of end-to-end deep learning approaches in machine transl...

Please sign up or login with your details

Forgot password? Click here to reset