On-Device Neural Language Model based Word Prediction

Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

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 on mobile devices for on-device keyboards poses computation as a bottle-neck due to their puny computation capacities. In this work, we propose an on-device neural language model 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. The proposed model outperforms existing methods for word prediction in terms of keystroke savings and word prediction rate and has been successfully commercialized.

Original languageEnglish
Title of host publicationCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations
PublisherAssociation for Computational Linguistics (ACL)
Pages128-131
Number of pages4
ISBN (Electronic)9781948087537
StatePublished - 2018
Event27th International Conference on Computational Linguistics, COLING 2018 - Santa Fe, United States
Duration: 20 Aug 201826 Aug 2018

Publication series

NameCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations

Conference

Conference27th International Conference on Computational Linguistics, COLING 2018
Country/TerritoryUnited States
CitySanta Fe
Period20/08/1826/08/18

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