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.
“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]
Traditionally, writing systems mainly use diegetic information, predicting text based only on the preceding text.
“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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Zhang, M. R., Wen, H., Cui, W., Zhu, S., Schwartz, H. A., Bi, X., & Wobbrock, J. O. (2021) · Human-computer interaction series · doi:10.1007/978-3-030-82681-9_5 · Cited by 4 · see citing works
Self-citationLi, Y., & Feit, A. M. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3749520 · Cited by 3 · see citing works
A Dataset of Noisy Typing on QWERTY Keyboards
Vertanen, K., & Kristensson, P. O. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581754.3584174 · Cited by 2 · see citing works
Efficient Typing on Ultrasmall Touch Screens With In Situ Decoder and Visual Feedback
Min, K.-B., & Seo, J. (2021) · IEEE Access · doi:10.1109/access.2021.3081173 · Cited by 2 · see citing works
Textflow : Toward Supporting Screen-free Manipulation of Situation-Relevant Smart Messages
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Probabilistic Text Entry—Case Study 3
Vertanen, K. (2021) · ACM eBooks · doi:10.1145/3447404.3447420 · Cited by 2 · see citing works
Exploring the impact of word prediction assistive features on smartphone keyboards for blind users
Alnfiai, M., & Kabir, M. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36653 · Cited by 3 · see citing works
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Zhang, D., Ma, Y., Dausch, G., Seiple, W., Gu, D., Ramakrishnan, I. V., & Bi, X. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747699 · Cited by 1 · see citing works
Self-citationFive Ways Function Models Enable Accessible Mixed Reality Interfaces
Kristensson, P. O. (2024) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct64951.2024.00054 · Cited by 1 · see citing works
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Promptor: An Interactive and Conversational Prompting Agent for Intelligent Text Entry TechniquesPreprint
Shen, J., Dudley, J. J., Zheng, J., Byrne, B., & Kristensson, P. O. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4690829 · Cited by 1 · see citing works
An Embedded Deep Learning based Word PredictionPreprint
Yu, S., Kulkarni, N. V., Lee, H., & Kim, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01662
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617479
Impacts of Predictive Text on Writing Content
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Desktop Metaphors, Window Managers, and Icons
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617463
Forms, Property Sheets, and Dialog Boxes
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617461
Scrolling and One-Dimensional Input
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617457
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Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617472
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617487
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Text Entry and Editing for Computers and Handhelds
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617460
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Future of Interaction Techniques
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Methods for Designing and Evaluating Interaction Techniques
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617473
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617482
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Ubiquitous Computing, Physical Widgets, Sensors, Wearables, and Internet of Things (IoT)
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- 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.
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