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Incremental Methods for Efficient and Personalized Text Entry in Noisy Incremental Methods for Efficient and Personalized Text Entry in Noisy Input Environments Andrew H. W. Fowler M. A. Mathematics, Washington State University, 2006 Presented to the Center for Spoken Language Understanding within the Oregon Health & Science University School of Medicine in partial fulfillment of the requirements for the degree Doctor of Philosophy in Computer Science & Engineering March 2020 Copyright c 2020 Andrew H. W. Fowler All rights reserved ii Center for Spoken Language Understanding School of Medicine Oregon Health & Science University CERTIFICATE OF APPROVAL This is to certify that the Ph. D. dissertation of Andrew H. W. Fowler has been approved. Steven Bedrick, Thesis Advisor Associate Professor, OHSU Brian Roark Senior Staff Research Scientist, Google Melanie Fried-Oken Professor, OHSU Peter Heeman Associate Professor, OHSU Xubo Song Professor, OHSU Shumin Zhai Principal Scientist, Google iii Acknowledgements The two people most directly responsible for the success of my work at CSLU have been Steven Bedrick and Brian Roark, both of whom served as my advisors. I owe them each a deep well of thanks and gratitude for their wisdom, unfailing kindness, and perseverance. Their guidance has been a consistent reminder of the power of hard work and of asking the right questions. To my other committee members I also owe many thanks|to Melanie Fried-Oken for her clinical insights, to Peter Heeman for having my back on multiple occasions, to Xubo Song for graciously volunteering to join my committee on short notice, and to Shumin Zhai for his mentorship at Google and his deep expertise. Many thanks are also due to the CSLU faculty and staff that have guided and helped me over the years, in particular Richard Sproat, Kyle Gorman, and Pat Dickerson. I also will miss the RSVP Keyboard team at OHSU and NEU, most notably Barry Oken, Betts Peters, Glory Noethe, Aimee Mooney, Deniz Erdo˘gmu¸s,Umut Orhan, and Greg Bieker (who made it all possible). CSLU is a small program in a small graduate school but its students are of the highest quality. I will never forget my fellow graduate students, both current and former, for their friendship and esprit de corps. Of special significance to me have been Meysam Asgari, Russ Beckley, Nate Bodenstab, Shiran Dudy, Aaron Dunlop, Maider Lehr, Meg Mitchell, Masoud Rouhizadeh, Golnar Sheikhshab, Emily Tucker Prud'hommeaux, and Mahsa Yarmohammadi. To Wally Tseng, Brice Tebbs, and Sabrina Burleigh I wish to offer my deep gratitude for allowing me to work full-time jobs while still finishing my PhD. There is no way I could have finished without their accommodation and kindness and I hope they realize how much it has mattered. To my wife's relatives I offer my thanks for reminding me every Christmas, Thanksgiving and Easter dinner just how excited they were that I was getting my PhD. In particular I want to give a very special thanks to Al Burmester for being my biggest cheerleader (and for a hundred cans of grape soda). I want to thank my parents, who in the middle of the Great Recession said to me \why don't you go back to school?" and without whom I quite literally would not be here. Thank you to Peter and Ben for being the best brothers, and thank you to my kids for reminding me that sometimes going outside is more important than algorithms. Finally, I want to thank my wife Kaci for her iv endless and unwavering support these many years. People act like having a job and kids while going to graduate school is some kind of juggling act, but it really isn't: It's about having somebody willing to cover for you, to sacrifice for you, to pick up the slack for you in thousand ways both large and small. Kaci has been that person for me, and for that I can only offer my love and gratitude. v Contents Acknowledgements ::::::::::::::::::::::::::::::::::::::: iv Abstract :::::::::::::::::::::::::::::::::::::::::::::: xi 1 Introduction ::::::::::::::::::::::::::::::::::::::::: 1 1.1 Research Objectives . .2 1.2 Contributions . .3 1.3 Organization of the Thesis . .4 2 General Background :::::::::::::::::::::::::::::::::::: 5 2.1 Incremental Methods & Analysis . .5 2.1.1 Psycholinguistics . .5 2.1.2 Parsing and Tagging in NLP . .6 2.1.3 Dialogue Systems & Speech Recognition . .8 2.1.4 Machine Translation . .9 2.2 General Language Model Adaptation . .9 2.3 Interfaces for AAC Text Entry . 10 2.3.1 Scanning Interfaces . 10 2.3.2 Brain Computer Interfaces . 11 2.4 Language Models for Text Entry . 12 2.4.1 Model Adaptation in Text Entry . 14 3 Technical Background ::::::::::::::::::::::::::::::::::: 16 3.1 Language Modeling . 16 3.1.1 ngram Language Models . 16 3.1.2 Neural Language Models . 18 3.1.3 Evaluation . 20 3.2 RSVP Keyboard . 22 3.2.1 ERP Classification . 23 3.2.2 Language Modeling . 24 3.2.3 Language Model and EEG Fusion . 24 3.2.4 System meta-parameters . 25 vi 4 Full History Fusion ::::::::::::::::::::::::::::::::::::: 26 4.1 RSVP Keyboard Failures . 26 4.2 Formulating a Solution . 27 4.3 Details of FHF . 29 4.3.1 Definitions . 29 4.3.2 Combining Evidence from LM and EEG . 29 4.3.3 Deriving Conditional Posterior Probabilities . 31 4.3.4 Practical Considerations for Computation . 31 4.4 FHF as an Algorithm . 32 4.4.1 Worked Example . 33 5 Applied Full History Fusion: BCI Simulation :::::::::::::::::::: 37 5.1 Materials and Methods . 37 5.2 Data and Evaluation . 38 5.3 Parameter Optimization . 39 5.4 Model Variants . 39 5.5 Results . 40 5.5.1 Baseline Results . 40 5.5.2 Full History Fusion Results . 41 5.6 Analysis . 43 5.6.1 The Backspace Trade-off . 43 5.6.2 Autotyping and Autodelete . 44 5.7 Discussion . 45 6 Applied Full History Fusion: Button Press Experiment :::::::::::::: 47 6.1 Materials and Methods . 47 6.1.1 Task Data Setup . 48 6.1.2 Subjects and Protocol . 49 6.2 Modeling Button Presses . 51 6.2.1 Reaction Time Model . 51 6.2.2 Parameter Estimation . 52 6.2.3 From Taps to Probabilities . 54 6.2.4 Input Noise . 55 6.3 Results . 57 6.3.1 Sequences per Character . 57 6.3.2 Typing Time . 59 6.3.3 Subjective Scores . 59 6.3.4 Analysis of exGaussian Curves . 60 6.4 Discussion . 62 vii 7 Backspace as a Sequence Modeling Problem ::::::::::::::::::::: 63 7.1 Introduction . 63 7.2 Model Setup . 64 7.2.1 Baseline Models . 65 7.2.2 Neural Models . 65 7.2.3 Errors in Training and Test . 67 7.2.4 Data Augmentation . 68 7.3 Simulated Data Generation . 69 7.3.1 Model States . 69 7.3.2 Error Modes . 71 7.4 Experiments and Results . 71 7.4.1 Corpora and Model Details . 71 7.4.2 Evaluation . 72 7.4.3 Primary Results and Analysis . 72 7.5 Other Variants and Analysis . 76 7.5.1 Future Work . 83 8 Personalization in Text Entry :::::::::::::::::::::::::::::: 84 8.1 Introduction . 84 8.2 Research Methodology . 86 8.3 Data . 87 8.3.1 Background Language Model . 87 8.3.2 Enron Corpus as an Evaluation Set . 87 8.3.3 Development and Test Sets . 88 8.4 Simulation . 89 8.4.1 Simulated Smart Touch.
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