The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
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%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Estimation of probabilities from sparse data for the language model component of a speech recognizer
Katz, S. M. (1987) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1987.1165125 · Cited by 1,664
Improved backing-off for M-gram language modeling
Kneser, R., & Ney, H. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1995.479394 · Cited by 1,471
A bit of progress in language modeling
Goodman, J. (2001) · Computer Speech and Language · doi:10.1006/csla.2001.0174 · Cited by 744
Klimt, B., & Yang, Y. (2004) · Conference on Email and Anti-Spam · Cited by 575
Text Entry for Mobile Computing: Models and Methods, Theory and Practice
MacKenzie, I. S., & Soukoreff, R. W. (2002) · Human-Computer Interaction · doi:10.1207/s15327051hci172&3_2 · Cited by 571
A cache-based natural language model for speech recognition
Kühn, R., & Mori, R. D. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56193 · Cited by 502
Kristensson, P.-O., & Zhai, S. (2004) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1029632.1029640 · Cited by 419
Self-citationDasher---a data entry interface using continuous gestures and language models
Ward, D. J., Blackwell, A. F., & Mackay, D. (2000) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/354401.354427 · Cited by 416
Shorthand writing on stylus keyboard
Zhai, S., & Kristensson, P.-O. (2003) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/642611.642630 · Cited by 371
Self-citation
Flagged first, then by citations
Research works printed but not linked: 9
- 4 . Charniak E. Blaheta D. Ge N. Hall K. Hale J. and Johnson M. BLLIP 1987--89 Wall Street Journal Corpus Release 1. 4. Charniak E. Blaheta D. Ge N. Hall K. Hale J. and Johnson M. BLLIP 1987--89 Wall Street Journal Corpus Release 1.
- Chelba C., 2010, Proc. Interspeech, ISCA
- 10.5555/839274.839354 · DOI
- Jurafsky D., 2008, Speech & Language Processing
- 10.5555/1628195.1628202 · DOI
- Klimt B., 2004, Proceedings of the Conference on Email and Anti-Spam, CEAS '04
- Kristensson P. O., 2011, Proc. Interspeech
- Sandu O., 2010, Proceedings of the Workshop on Domain Adaptation for Natural Language Processing, Association for Computational Linguistics
- Stolcke A., 1998, Proceedings of News Transcription and Understanding Workshop, DARPA
Cited by
Sorted by citations
Items 101–128 of 128
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617459
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617485
Undo, Redo, Repeat, Cancel, Command History, and Macro Recording
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617467
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617475
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617480
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617489
Percent-done Progress Indicators
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617458
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617449
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617452
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617494
Creating, Selecting, and Manipulating Objects
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617462
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617484
Interview: David Canfield Smith
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617476
Interactions in 3D, Virtual and Augmented Reality, and Games
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617464
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617490
Formal and Informal Models, Metaphors, and Patterns for the Design of Interaction Techniques
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617471
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617474
Interaction Techniques for People with Disabilities
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617469
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617450
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617454
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617481
Interaction Techniques for Help
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617468
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617451
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617492
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617477
Myers, B. A. (2024) · ACM eBooks · doi:10.1145/3617448.3617478
What smartphones change about writing: the impact of word suggestions on orthographic processing
Anastaseni, A., Roy, Q., Perret, C., Romano, A., & Kandel, S. (2025) · Cognitive Neuropsychology · doi:10.1080/02643294.2025.2604374
Learning from Natural Language Feedback for Personalized Question Answering
Salemi, A., & Zamani, H. (2025) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3805712.3809747 · Cited by 7
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 ›
Are you an author, or do you see a problem with this page? Request a review
