This work proposes a novel metric, Maximally Amortized Cost (MAC), for cost evaluations of error correction of predictive Chinese input methods (IMs). With a series of real-time sim- ulation, user correction behaviors are analyzed by estimating generalized backward compati- bility of adaptive Chinese IMs. Comparisons between three IMs by using MAC with differ- ent context lengths report empirical factors of context length for improving predictive IMs. The error-tolerance level—Futile Effort, Ben- eficial Effort and Utility—of adaptive IMs is also proposed and analyzed.

1. ANALYSIS
Mike Tian-Jian Jiang, Cheng-Wei Lee, Chad Liu, Yung-Chun Chang,
Wen-Lian Hsu
Institute of Information Science, Academia Sinica
OF
ADAPTIVE
ROBUSTNESS
入力中
文

2. 入力, Input Method (IM)
Text Entry
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3. 11/13/11IIS, Sinica 3/30
http://www.gadgetvenue.com/samsung-omnia-2-swype-text-entry-system-
11243013/
http://www.inquisitr.com/97719/circboard-brings-easier-text-entry-to-xbox-360-just-needs-
investors/
http://myapplenewton.blogspot.com/2010/01/text-entry-speed-face-off.html
http://research.microsoft.com/en-
us/um/redmond/groups/cue/mobileinteraction/
http://en.wikipedia.org/wiki/File:Palm_Graffiti_gestures.png
http://wol.inf.phy.cam.ac.uk/TryJavaDasherNow.html

4. Radical vs. Phonetic
Homographs vs. Homophones
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5. Disambiguation
To predict or not
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http://thereality.nl/potd/1552/donderdag-31-december-2009.html http://www.ehow.com/how_6788906_use-multi_tap.html http://en.wikipedia.org/wiki/File:ITap_on_Motorola_C350.jpg

6. HCI, NLP (, SE)
• Unified error metrics (Soukoreff
and MacKenzie, 2001)
• Error correction (Arif and
Stuerzlinger, 2010)
• Reused vocabulary (Tanaka-
Ishii et al., 2003)
• Backward compatibility (Suzuki
and Gao, 2005)
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http://orionwell.files.wordpress.com/2007/07/hal-9000.jpg

7. “Prediction and spell correction can be very annoying if
they are not smart enough. For many applications, user
input can be very noisy (imagine voice recognition or
typing on a small screen), so the input methods must be
robust against such noise. Finally, there is no standard
data set or evaluation metric, which is necessary for
quantitative analysis of user input experience.”
– WTIM 2011 statements of call for
papers
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8. Prediction & Adaptation
Properties of Chinese Phonetic IM
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9. Adaptation via
Online Implicit User Feedback
(Online × Offline × Implicit × Explicit) user feedback
Adaptation procedure extends Tanaka-Ishii et al. (2003)
User → ambiguous source keystroke string s
IM retrieve(s ∈ D) → candidate chunks c[]; D ≡ {db ∪ profile}
IM sort(c[])
IM compose(c[]) → target string t ∝ eval(t)
User modify(t) → t’
IM adapt(t’ ∖ t) → {feedback ∪ profile}
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http://www.johnehrenfeld.com/2009/05/be-careful-with-adaptation.html

10. Dilemma
Type long and (either right or wrong things) prosper
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http://www.personal.psu.edu/afr3/blogs/SIOW/2011/10/live-long-and-prosper.html

11. Amortized Cost
Trade-off between benefic and cost of error correction
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12. …based on
Unified Error Metrics
Related to
minimum string distance (MSD) error and
key-stroke per character (KSPC)
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With
Fitts’ law and Hick’s law
12/30

13. Notations
P: presented text
T: transcribed text
IS: input stream
C: number of correct characters in T
F: keystrokes for fixing in IS like editing, modifier, or navigation.
INF: number of incorrect yet not fixed errors in T
IF: number of incorrect but fixed errors (keystrokes in IS that
are not F and not in T)
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14.  P: the quick brown fox
 T: the quixck brwn fox
 MSD(P, T) = 2 here
 Only for T without editing process
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MSD(P,T)
SA
´100%

15. KSPC
|IS| / |T|
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16.  T: the quixck brwn fox
 T’: the quixck brown fox
 Total Error Rate(T’) = (2/18)%
 MSD Error Rate(T’) = (1/17)%
 KSPC(T’) = 19/17
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Total Error Rate =
INF + IF
C + INF + INF
´100%
MSDError Rate =
INF
C + INF
´100%
KSPC »
C + INF + IF + F
C + INF

17.  t: time
 a, b: empirical constants
 d: distance to target
 w: width of target
 n: number of equal possible choices
 Index of difficulty (ID): log2((d/w) + 1)
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tF
= a+ blog2
(
d
w
+1)
tH
= blog2
(n+1)

18. Error Correction Conditions
None, Forced, or Recommended conditions
No relations between typing speed and correction attempt
Spectrum of Recommended condition
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Situation Fixed
characters
INF IF F
S0 none INF0 0 0
Si some INFi IFi Fi
Sall all 0 IFall Fall
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19. 11/13/11 19/30IIS, Sinica
AC =
Wasted Bandwidth
Utilized Bandwidth
=
INF + IF + F
C + INF + IF + F
C
C + INF + IF + F
=
INF + IF + F
C
INF0
C
£ ACi
=
INFi
+ IFi
+ Fi
C
£
IFall
C
+
Fall
C
=
INF0
C
+
Fall
C
penalty =
tH
´ INF0
+tF
´max(D)
C + INF0
reward =
C
C + INF0
ACmodification
=
penalty
reward
=
tH
´ INF0
+tF
´max(D)
C
MAC =
INF0
C
+ ACmodification

20. Vocabulary Reuse
70 – 80 % vocabulary reused only after a small offset window in KB
(such that simulations of typing repeatedly are representative
enough)
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21. Backward Compatibility
Error Ratio = |errors by adaptation| / |corrections by adaptation|
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22. … however,
Error correction can be complicated.
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23. Input
Correction
Process of input Process of Correction
[User corrects]
[User verify]
[User doesn’t verify]
[There are errors]
[User doesn’t correct]
[User corrects]
[There are no errors]
[User verify]
[User doesn’t correct]
[User doesn’t verify]
[There are no errors]
i
onverificati
[There are errors]
c
onverificati
hi
error
si
error
,,
 
i
correction
c
correction
hc
error
sc
error
,,
 
c
correction
c
onverificati
i
correction
i
onverificati
h
correction  
hc
error
sc
error
hi
error
si
error
h
error
s
error
,,,,
 

24. Simulation
3 IMs A, B, and C
Phonetic method
Bopomofo (Zhuyin)
Daqian keyboard layout
Data
Academia Sinica Balanced Corpus
4,000 sentences
39,469 words
Independent variables
Context length k
ρh
correction
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25. Comparison of MAC
IM-A seems to be different to others?
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26. GBC at Context Length 6
Again, IM-A is segregated……
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27. …more aspects wanted
Than this V curve…
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28. Error Tolerance
Level
• Futile Effort (Ef): |never adapted chunks|
• Beneficial Effort (Eb):|adapted chunks|
• Utility (U):|before forgotten|
• Eb(IM-A) vs. Eb(IM-B)
• “his” or “her” or “its”?
• 他 or 她 or 它
• /ta1/
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Correlation Coefficient to CAR
Ef
avg Eb
avg Uavg
IM-A 0.49 0.92 0.66
IM-B -0.78 -0.62 -0.51
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29. How about a shared task?
Just my humble suggestion :p
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http://xkcd.com/114/

30. Thank YOU…?
Or do we have time for…
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31. Many MORE Things…
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32. zhi1-shi4
tou2-shi4
dao4-shi4
fang1-shi4
chang2-shi4
yi4-shi4
zhi4-shi4
shi4-wei4
shi4-li4
shi4-shang4
shi4-gu4
shi4-yi2
shi4-ji1
shi4-zhong1
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33. Reduced n-gram
British Rail Enquiries
P(Enquiries|British, Rail)
P(Enquiries|Rail)
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34. OpenVanilla
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35. SearchTyping
http://www.youtube.com/watch?v=jgl23E6wzVA
http://www.youtube.com/watch?v=xJsXaPe_f8c
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36. Thank YOU!
Any question or comment?
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