Platform for Studying Self-Repairing Auto-Corrections in Mobile Text Entry based on Brain Activity, Gaze, and Context
Veröffentlichungsdatum
2020
Zusammenfassung
Auto-correction is a standard feature of mobile text entry. While the performance of state-of-the-art auto-correct methods is usually relatively high, any errors that occur are cumbersome to repair, interrupt the flow of text entry, and challenge the user's agency over the process. In this paper, we describe a system that aims to automatically identify and repair auto-correction errors. This system comprises a multi-modal classifier for detecting auto-correction errors from brain activity, eye gaze, and context information, as well as a strategy to repair such errors by replacing the erroneous correction or suggesting alternatives. We integrated both parts in a generic Android component and thus present a research platform for studying self-repairing end-to-end systems. To demonstrate its feasibility, we performed a user study to evaluate the classification performance and usability of our approach.
Schlagwörter
Text entry
;
Auto-correction
;
Self-repair
;
Eye gaze
;
EEG
Verlag
ACM
Fachbereich
Dokumenttyp
Artikel/Aufsatz
Zeitschrift/Sammelwerk
Startseite
1
Endseite
13
Zweitveröffentlichung
Ja
Dokumentversion
Postprint
Lizenz
Sprache
Englisch
Dateien![Vorschaubild]()
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Name
Putze_Platform for studying Self-repairing auto-correction_2020_accepted-version_PDF-A.pdf
Size
3.32 MB
Format
Adobe PDF
Checksum
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