On-Device Neural Language Model Based Word Prediction

Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim


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 the 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 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. Our proposed model outperforms the existing methods for word prediction in terms of keystroke savings and word prediction rate and has been successfully commercialized.
Anthology ID:
C18-2028
Volume:
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
128–131
Language:
URL:
https://aclanthology.org/C18-2028
DOI:
Bibkey:
Cite (ACL):
Seunghak Yu, Nilesh Kulkarni, Haejun Lee, and Jihie Kim. 2018. On-Device Neural Language Model Based Word Prediction. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 128–131, Santa Fe, New Mexico. Association for Computational Linguistics.
Cite (Informal):
On-Device Neural Language Model Based Word Prediction (Yu et al., COLING 2018)
Copy Citation:
PDF:
https://aclanthology.org/C18-2028.pdf
Code
 meinwerk/WordPrediction