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Effects of Language Modeling and its Personalization on Touchscreen Typing Performance
Open accessAbstract
Modern smartphones correct typing errors and learn user-specific words (such as proper names). Both techniques are useful, yet little has been published about their technical specifics and concrete benefits. One reason is that typing accuracy is difficult to measure empirically on a large scale. We describe a closed-loop, smart touch keyboard (STK) evaluation system that we have implemented to solve this problem. It includes a principled typing simulator for generating human-like noisy touch input, a simple-yet-effective decoder for reconstructing typed words from such spatial data, a large web-scale background language model (LM), and a method for incorporating LM personalization. Using the Enron email corpus as a personalization test set, we show for the first time at this scale that a combined spatial-language model reduces word error rate from a pre-model baseline of 38.4% down to 5.7%, and that LM personalization can improve this further to 4.6%.
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“Earlier work mainly focused on single word suggestions [11,12,18,34].”
From Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting (2023) · cites this paper as [11]
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“Traditionally, systems mainly use diegetic information, that is, they predict text based (only) on the preceding text [11,12,18,34].”
From Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting (2023) · cites this paper as [11]
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Metadata
- Authors
- Fowler, A., Partridge, K., Chelba, C., Bi, X., Ouyang, T., & Zhai, S.
- Affiliations
- Oregon Health & Science University (US)
- Google (United States) (US)
- Venue
- Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2015-04-18
- Pages
- 649-658
- OpenAlex
- W2051780479
- Cited by
- 127
- References
- 35
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 54 · 2023: 10 · 2022: 10 · 2021: 10 · 2020: 6 · 2019: 8 · 2018: 6 · 2017: 7 · 2016: 6
- FWCI
- 4.79
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- National Institutes of Health (R01 DC009834)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Human-Computer Interaction › Interactive and Immersive Displays
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- language modeling, personalization, word error rate, typing accuracy, proper names
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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