Accelerating Text Communication via Abbreviated Sentence Input

Jiban Adhikary, Jamie Berger, Keith Vertanen

Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021.

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 also 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 noisy input where users often omit spaces and mid-word vowels. We show using neural language models for selecting training text and rescoring 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 were 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.