Accelerating Text Communication via Abbreviated Sentence Input

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 midword 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.

Context-Aware Abbreviation Expansion Using Large Language Models

“…Abbreviation expansion for text entry. Previous research on aiding text entry through AE used abbreviation schemes such as using only content words (Demasco and McCoy, 1992), discarding certain vowels and consonants (Shieber and Nelken, 2007), and flexible letter saving schemes (Pini et al, 2010;Adhikary et al, 2021;Gorman et al, 2021). Spontaneous abbreviations schemes primarily omit vowels, repeating consonants, last characters, and spaces, and lead to modest KSR (e.g., 25-40% in Willis et al 2005, and 21% in Adhikary et al 2021.)…”

“…(2021) achieve a word error rate of 1.5%. Adhikary et al (2021) report a 24.2% top-5 sentence error rate decoding abbreviations using an RNN to augment an n-gram LM. Our presented approach is a step towards using automation and context to expand abbreviations at a higher KSR that is close to that of SMS language.…”