Barcelona, Spain | September 3, 2018 MobileHCI 2018 Workshop on Socio-Technical Aspects of Text Entry
Text Entry Does Not Imply “English
Text Entry”
Luis A. Leiva Abstract
Sciling, SL The title above is an actual sentence borrowed from
46120 Valencia, Spain MacKenzie and Soukoreff’s seminal article on Text Entry
name@sciling.com for Mobile Computing (2002), which I found to be
enlightening many years ago, and has inspired my latest
work on text entry.
My latest work on text entry has been directed at
improving the stimulus phrases when conducting
evaluations with non-native English speakers. Together
with my co-authors, I devised a couple of automated
phrase sampling methods that produce phrases that are
memorable (easy to remember), representative of the
language or task, and easy to assimilate. This paper
provides a brief overview of such methods as well as
other contributions I have made to the text entry
community.
Author Keywords
Text Entry; Phrases; Sampling; Multilingualism;
Permission to make digital or hard copies of part or all of this work for personal or Memorability; Representativeness; Semantics.
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for third-party components of this work must be honored. ACM Classification Keywords
For all other uses, contact the owner/author(s).
H.5.m [Information interfaces and presentation]:
Copyright held by the owner/author(s).
MobileHCI, 2018 Barcelona, Spain.
Miscellaneous.
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Barcelona, Spain | September 3, 2018 MobileHCI 2018 Workshop on Socio-Technical Aspects of Text Entry
Biography
Luis Leiva is working as researcher and Chief
Technological Officer at Sciling, a Machine Learning
company based in Spain. He is a former research staff
member of the PRHLT research center at the Technical
University of Valencia (UPV), where he worked as
Principal Investigator. He got his PhD degree (with
honors, international mention, extraordinary doctorate
award) in Computer Science in 2012. He also has two
undergraduate degrees—Industrial Design and Figure 1: BoD Taps. Top row: shoulder surfer view. Bottom
Industrial Engineering—and an M.Sc. in row: front view.
Communications and Mobile Services Development
(2006) from the UPV. His research interests include
Human-Computer Interaction, Machine Learning, and Tiny QWERTY Keyboards
the intersection between the two. His recent work on With the ongoing breakthrough of wearables, such as
text entry includes Back-of-Device interaction [1, 3, 4, 5], smartwatches or digital jewelry, text entry on devices
new miniaturized QWERTY keyboard layouts [6, 7], and with very small screens (1” wide or less) becomes
multilingual phrase sampling methods [2, 8, 9]. increasingly relevant and a challenging issue, simply
because space is at a premium. I contributed in this
Overview of Past and Current Work regard with ZShift [7], a callout-based text entry
While this position paper will focus on phrase sampling, technique for diminutive QWERTY keyboards. I also
I believe it is worth mentioning my previous work on text investigated cost-efficient error auto-correction
entry as well. mechanisms [6], that can be easily implemented in
current wearables.
Back-of-Device Interaction
I have investigated two contexts of use for
Back-of-Device (BoD) interaction. The first one is BoD
Taps [1, 5], an unlock technique that was found to be
very usable and theoretically more secure than their
peers. The second one is βTap [3, 4], a tap-based BoD
input technique that uses a low-cost yet highly
discriminative set of features from commodity sensors,
without the need to instrument the device.
Figure 2: ZShift keyboard on a tiny touchscreen device.
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[S] WIKI:EN:Journal_(1977_TV_series)
Barcelona, Spain | September 3, 2018 MobileHCI 2018 Workshop on Socio-Technical Aspects of Text Entry
[S] daybook#n#1
[S] take#v#6
binary_file#n#1
accounting_data#n#1
read-only_file#n#1
computer_file#n#1 interpret#v#1
[S] read#v#8
read-only_memory#n#1 [S] read#v#4
optical_disk#n#1
datum#n#1
person#n#1
firmware#n#1 memory#n#4
Multilingual Phrase Sampling web_site#n#1
operating_system#n#1
disk_cache#n#1 bulletin_board_system#n#1
web_log#n#1
home_computer#n#1 bit#n#6
magnetic_tape#n#1 [S] read#v#11
This is the current line of research I am mostly involved internet#n#1
random-access_memory#n#1
electronic_mail#n#1
on-line#a#2
homo#n#2
diskette#n#1
data_structure#n#1 algorithm#n#1
computer#n#1
pari-mutuel_machine#n#1
in, motivated by the fact that text entry is fundamentally cognition#n#1 [S] diary#n#1 [S] read#v#3
interaction#n#1
WIKI:EN:Digital
program#n#7
[S] WIKI:EN:Interaction_(album)
language#n#1
multilingual. Many text entry researchers are conducting least_squares#n#1
WIKI:EN:Human–computer_interaction
WIKI:EN:Confidence_interval
user studies in many languages different from English, [S] WIKI:EN:Interaction_(s
WIKI:EN:Multivariate_normal_distribution
WIKI:EN:Bias_of_an_estimator
WIKI:EN:Mean_squared_error
and the tasks given to participants are often computer_graphics#n#1
computer_network#n#1
radio#n#1
programming_language#n#1 engineering#n#2
regression_analysis#n#1
personal#a#1
covariance#n#1
analysis_of_variance#n#1
computer_science#n#1
telecommunication#n#1
language-sensitive. This suggests that, when resolution#n#7
virtual_reality#n#1
telephone#n#2
data#n#1
programmer#n#1
[S] understand#v#3
reading#n#1
read#v#1 literate#n#1
reader#n#1
WIKI:EN:Gauss–Markov_theorem
variance#n#3
normal_distribution#n#1
conducting text entry experiments with non-native information_technology#n#1
mathematics#n#1
written_record#n#1
journal#n#2 expert#n#1
diarist#n#1
reader#n#3 statistic#n#1
electronics#n#1 number_cruncher#n#1
[S] WIKI:EN:Computer_engineering artificial_intelligence#n#1
subtracter#n#1
English speakers or in very specialized domains (e.g. musical_instrument_digital_interface#n#1 electrical_engineering#n#1
energy#n#1
chemistry#n#1
physics#n#1
automaton#n#2
boson#n#1
computer_user#n#1 statistics#n#1
statistician#n#2
proportionality#n#1
adder#n#1
WIKI:EN:Will_Gregory [S] WIKI:EN:Computer_music
medical devices, where technical vocabulary is [S] WIKI:EN:Human_(Goldfrapp_song)
WIKI:EN:Electronic_music
Figure 3: The Kaps technique uses knowledge graphs as a
industrial_engineering#n#1
physical_phenomenon#n#1
electromagnetism#n#2 science#n#1
machine#n#1
personal_digital_assistant#n#1
fermion#n#1
force#n#2
WIKI:EN:Goldfrapp charge#n#4
cyborg#n#1
[S] WIKI:EN:Human_(1971_film)
commonplace), we either use a standard phrase set common concept representation across languages.
quantum_mechanics#n#1
emotion#n#1
android#n#1 genetics#n#1
society#n#1
diary#n#2
and accept that there will be differences in performance discipline#n#1
personal_computer#n#1
brain#n#1 reciprocal#a#1
man#n#3
record#n#5
character#n#4
WIKI:EN:Natural_environment male#n#2
across studies, or we have to develop language- or elementary_particle#n#1
[S] interaction#n#3
health#n#1
cell#n#2 soul#n#3
organism#n#1
creature#n#2 give#v#18
[S] read#v#9
stimulus phrases may have in current text entry
general_relativity#n#1
domain-specific phrase sets. To solve this problem,
[S] WIKI:EN:Human_(The_Killers_song)
cosmic_background_radiation#n#1
phenotype#n#1
matter#n#3
life#n#11
human_body#n#1
annals#n#1
[S] journal#n#4
automated phrase sampling methods like MemRep and evaluations. Moreover, the phrase sampling techniques [S] learn#v#4
universe#n#1
animal#n#1
evolution#n#2
human#a#1
[S] journal#n#6
Kaps become necessary. I created so far have been tested with Indo-European ecosystem#n#1
nature#n#3
mind#n#1 virus#n#1
bacteria#n#1
humanness#n#1
object#n#1
languages only, which represent an important portion of spirit#n#4
On the one hand, MemRep [8, 9] was the first phrase the current living languages but certainly there are may study#n#2
hog#n#3
blood#n#1
sampling method that optimized both for memorability more languages worth of evaluation. I look forward to [S] read#v#5
and representativeness (dual-objective function). Our discussing this with the workshop attendees. intestine#n#1
[S] WIKI:EN:Human_(Stargate_Universe)
experimental results showed that it performed
significantly better than other sampling procedures. On
[S] WIKI:EN:Computer_(magazine)
REFERENCES
the other hand, Kaps [2] sought a balance between 1. Catalá, A., and Leiva, L. A. Back-of-device
memorability, representativeness, and complexity authentication with bod taps and bod shapes. In Proc.
(grammar and semantics subtleties). We found that, by ACM Conf. on Human-computer interaction with
introducing this third variable, the method not only had mobile devices and services (MobileHCI) (2014).
better properties than MemRep but also that the[S] journal#n#5
2. Franco-Salvador, M., and Leiva, L. A. Multilingual
resulting phrases exhibited better performance.
phrase sampling for text entry evaluations.
Proposed Scenario International Journal of Human-Computer Studies
I believe my recent research on text entry is of special [S] WIKI:EN:Human_(band)
113, 1 (2018).
[S] WIKI:EN:Human_(Brandy_Norwood_album)
relevance to text entry researchers interested in 3. Granell, E., and Leiva, L. A. Less is more: Efficient
conducting experiments tailored to the linguistic back-of-device tap input detection using built-in
capabilities of their participants. Therefore, I would like smartphone sensors. In Proceedings of the ACM Intl.
to contribute to the workshop with this topic. More [S] WIKI:EN:Human_(Babylon_5)
Conf. on Interactive Surfaces and Spaces (ISS)
concretely, I would like to dive in the importance that (2016).
[S] WIKI:EN:Projected
[S] WIKI:EN:Lights_(Ellie_Goulding_album)
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Barcelona, Spain | September 3, 2018 MobileHCI 2018 Workshop on Socio-Technical Aspects of Text Entry
4. Granell, E., and Leiva, L. A. βTap: Back-of-device
tap input with built-in sensors. In Proc. ACM Conf. on
Human-computer interaction with mobile devices and
services (MobileHCI) (2017).
5. Leiva, L. A., and Catalá, A. Bod taps: An improved
back-of-device authentication technique on
smartphones. In Proc. ACM Conf. on
Human-computer interaction with mobile devices and
services (MobileHCI) (2014).
6. Leiva, L. A., Sahami, A., Catalá, A., Henze, N., and
Schmidt, A. Error auto-correction mechanisms on
tiny qwerty soft keyboards. In CHI Workshop on Text
Entry on the Edge (2015).
7. Leiva, L. A., Sahami, A., Catalá, A., Henze, N., and
Schmidt, A. Text entry on tiny qwerty soft keyboards.
In Proc. SIGCHI Conf. on Human Factors in
Computing Systems (CHI) (2015).
8. Leiva, L. A., and Sanchis-Trilles, G. Representatively
memorable: sampling the right phrase set to get the
text entry experiment right. In Proc. SIGCHI Conf. on
Human Factors in Computing Systems (CHI) (2014).
9. Sanchis-Trilles, G., and Leiva, L. A. A systematic
comparison of 3 phrase sampling methods for text
entry experiments in 10 languages. In Proc. ACM
Conf. on Human-computer interaction with mobile
devices and services (MobileHCI) (2014).
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