A Connectionist Technique for Accelerated Textual Input: Letting a Network Do The Typing

Abstract

Each year people spend a huge amount of time typing. The text people type typically contains a tremendous amount of redundancy due to predictable word usage patterns and the text's structure. This paper describes a neural network system call Auto'Qpist that monitors a person's typing and predicts what will be entered next. Auto'Qpist displays the most likely subsequent word to the typist, who can accept it with a single keystroke, instead of typing it in its entirety. The multi-layer perceptron at the heart of Auto'Qpist adapts its predictions of likely subsequent text to the user's word usage pattern, and to the characteristics of the text currently being typed. Increases in typing speed of 2-35 when typing English prose and 10-20% when typing C code have been demonstrated using the system, suggesting a potential time savings of more than 20 hours per user per year. In addition to increasing typing speed, AutoTypist reduces the number of keystrokes a user must type by a similar amount (2-35 for English, 10-20% for computer programs). This keystroke savings has the potential to significantly reduce the frequency and seventy of repeated stress injuries caused by typing, which are the most common injury suffered in today's office environment.