Accelerating Text Communication via Abbreviated Sentence Input
Document Type
Conference Proceeding
Publication Date
2021
Department
Department of Computer Science
Abstract
Typing every character in a text message may require more time or effort than strictly necessary. Skipping spaces or other characters may be able to speed input and reduce a user’s physical input effort. This can be particularly important for people with motor impairments. In a large crowdsourced study, we found workers frequently abbreviated text by omitting mid-word vowels. We designed a recognizer optimized for expanding noisy abbreviated input where users often omit spaces and mid-word vowels. We show using neural language models for selecting conversational-style training text and for rescoring the recognizer’s n-best sentences improved accuracy. On noisy touchscreen data collected from hundreds of users, we found accurate abbreviated input was possible even if a third of characters was omitted. Finally, in a study where users had to dwell for a second on each key, sentence abbreviated input was competitive with a conventional keyboard with word predictions. After practice, users wrote abbreviated sentences at 9.6 words-per-minute versus word input at 9.9 words-per-minute.
Publication Title
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Recommended Citation
Adhikary, J.,
Berger, J.,
&
Vertanen, K.
(2021).
Accelerating Text Communication via Abbreviated Sentence Input.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers),
1, 6574-6588.
http://doi.org/10.18653/v1/2021.acl-long.514
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15764