1. Computer Aided Translation
Philipp Koehn
10 June 2010
Philipp Koehn Computer Aided Translation 10 June 2010
2. 1
Overview
• Volunteer Translation Projects
• Machine Translation
• Human Translation
• Assistance to Human Translators
• User Study 1
• User Study 2
Philipp Koehn Computer Aided Translation 10 June 2010
3. 2
Overview
• Volunteer Translation Projects
• Machine Translation
• Human Translation
• Assistance to Human Translators
• User Study 1
• User Study 2
Philipp Koehn Computer Aided Translation 10 June 2010
4. 3
Crowd Sourcing vs. Volunteers
• Successful volunteer collaboration translation projects
(initiated by a growing communities of self-interested participants)
– DE-News
– Chinese translations of Guardian etc.
– dotSUB
5. 3
Crowd Sourcing vs. Volunteers
• Successful volunteer collaboration translation projects
(initiated by a growing communities of self-interested participants)
– DE-News
– Chinese translations of Guardian etc.
– dotSUB
• Successful crowd sourcing translation projects
(initiated by an organization with a translation need)
– Google localization
– TED translations
Philipp Koehn Computer Aided Translation 10 June 2010
6. 4
DE-News
• Project
– transcription of German radio headline news
– translation into English
– about 5-10 stories per day, 1993-2003, http://www.germnews.de/
• Motivation
– initially Germans abroad wanted to stay informed about events in Germany
– also non-German speakers who were interested in Germany
– no lack of translators (mostly Germans), but of news gatherers
– mostly altruistic: interested in practicing language skills?
• used for statistical machine translation:
1 million word parallel corpus collected in 2002
Philipp Koehn Computer Aided Translation 10 June 2010
7. 5
Chinese Translations of Guardian
• Project
– largest open translation community in China, launched in 2006
– 90,000 contributors, 5,000 ”community translators”, 30,000 translations
– motivation: make English content available to Chinese readers
– http://www.yeeyan.org/
• Guardian translation project
– official collaboration with British Guardian news paper
– Dec 2009: translation of Guardian articles ”closed down by the Chinese
authorities”
Philipp Koehn Computer Aided Translation 10 June 2010
8. 6
dotSUB
• Project
– subtitling and translation platform, launched in 2007
– ”upload your video, add sub titles, translate subtitles”
– easy user interface, open to anybody
– service used by TED talks for their translations
• Content
– guides to Wikis, RSS, Twitter, ...
– documentations
– political opinion pieces
Philipp Koehn Computer Aided Translation 10 June 2010
9. 7
Overview
• Volunteer Translation Projects
• Machine Translation
• Human Translation
• Assistance to Human Translators
• User Study 1
• User Study 2
Philipp Koehn Computer Aided Translation 10 June 2010
10. 8
Statistical Machine Translation
• Learning from data (sentence-aligned translated texts)
German documents English documents
....Bank.................. ....bank..................
..........Bank............ ..........bench..........
........Bank.............. ........bank..............
.................Bank..... .................bank.....
p(bank|Bank) = 0.75, p(bench|Bank) = 0.25
• New machine translation systems can be built automatically
Philipp Koehn Computer Aided Translation 10 June 2010
11. 9
Phrase-Based Translation
• Foreign input is segmented in phrases
– any sequence of words, not necessarily linguistically motivated
• Each phrase is translated into English
• Phrases are reordered
Philipp Koehn Computer Aided Translation 10 June 2010
12. 10
Translation Options
er geht ja nicht nach hause
he is yes not after house
it are is do not to home
, it goes , of course does not according to chamber
, he go , is not in at home
it is not home
he will be is not under house
it goes does not return home
he goes do not do not
is to
are following
is after all not after
does not to
not
is not
are not
is not a
• Many translation options to choose from
Philipp Koehn Computer Aided Translation 10 June 2010
13. 11
Translation Options
er geht ja nicht nach hause
he is yes not after house
it are is do not to home
, it goes , of course does not according to chamber
, he go is not in at home
it is not home
he will be is not under house
it goes does not return home
he goes do not do not
is to
are following
is after all not after
does not to
not
is not
are not
is not a
• Many translation options to choose from
Philipp Koehn Computer Aided Translation 10 June 2010
14. 12
Decoding Process: Find Best Path
er geht ja nicht nach hause
yes
he
goes home
are
does not go home
it
to
Philipp Koehn Computer Aided Translation 10 June 2010
15. 13
Why Machine Translation?
Assimilation — reader initiates translation, wants to know content
• user is tolerant of inferior quality
• focus of majority of research (GALE program, etc.)
16. 13
Why Machine Translation?
Assimilation — reader initiates translation, wants to know content
• user is tolerant of inferior quality
• focus of majority of research (GALE program, etc.)
Communication — participants don’t speak same language, rely on translation
• users can ask questions, when something is unclear
• chat room translations, hand-held devices
• often combined with speech recognition, IWSLT campaign
17. 13
Why Machine Translation?
Assimilation — reader initiates translation, wants to know content
• user is tolerant of inferior quality
• focus of majority of research (GALE program, etc.)
Communication — participants don’t speak same language, rely on translation
• users can ask questions, when something is unclear
• chat room translations, hand-held devices
• often combined with speech recognition, IWSLT campaign
Dissemination — publisher wants to make content available in other languages
• high demands for quality
• currently almost exclusively done by human translators
Philipp Koehn Computer Aided Translation 10 June 2010
18. 14
Why Machine Translation?
Assimilation — reader initiates translation, wants to know content
• user is tolerant of inferior quality
• focus of majority of research (GALE program, etc.)
Communication — participants don’t speak same language, rely on translation
• users can ask questions, when something is unclear
• chat room translations, hand-held devices
• often combined with speech recognition, IWSLT campaign
Dissemination — publisher wants to make content available in other languages
• high demands for quality OUR
• currently almost exclusively done by human translators FOCUS
Philipp Koehn Computer Aided Translation 10 June 2010
20. 15
Goal: Helping Human Translators
If you can’t beat them, join them.
• How can machine translation help human translators?
21. 15
Goal: Helping Human Translators
If you can’t beat them, join them.
• How can machine translation help human translators?
• First question: What do translators do?
Philipp Koehn Computer Aided Translation 10 June 2010
22. 16
Overview
• Volunteer Translation Projects
• Machine Translation
• Human Translation
• Assistance to Human Translators
• User Study 1
• User Study 2
Philipp Koehn Computer Aided Translation 10 June 2010
23. 17
Setup
• 10 students at the University of Edinburgh
– half native French speakers
– half native English speakers with advanced French
24. 17
Setup
• 10 students at the University of Edinburgh
– half native French speakers
– half native English speakers with advanced French
• Each student translated
– news stories
– French-English
– about 40 sentences
– easy task: familiar content, no specialized terminology
• Keystroke log
Philipp Koehn Computer Aided Translation 10 June 2010
25. 18
Keystroke Log
Input: Au premier semestre, l’avionneur a livr 97 avions.
Output: The manufacturer has delivered 97 planes during the first half.
(37.5 sec, 3.4 sec/word)
black: keystroke, purple: deletion, grey: cursor move
height: length of sentence
Philipp Koehn Computer Aided Translation 10 June 2010
27. 19
Analysis
• We can observe
– slow typing
– fast typing
28. 19
Analysis
• We can observe
– slow typing
– fast typing
– pauses
29. 19
Analysis
• We can observe
– slow typing
– fast typing
– pauses
• Pauses
– beginning pause: reading the input sentence
– final pause: reviewing the translation
30. 19
Analysis
• We can observe
– slow typing
– fast typing
– pauses
• Pauses
– beginning pause: reading the input sentence
– final pause: reviewing the translation
– short pauses (2-6 seconds): hesitation
– medium pauses (6-60 seconds): problem solving
– big pauses (>60 seconds): serious problem
Philipp Koehn Computer Aided Translation 10 June 2010
31. 20
Time Spent on Activities
Pauses
User total initial final short medium big keystroke
L1a 3.3s 0.1s 0.1s 0.2s 1.0s 0.1s 1.8s
L1b 7.7s 1.3s 0.1s 0.3s 1.8s 1.9s 2.3s
L1c 3.9s 0.2s 0.2s 0.3s 0.7s - 2.5s
L1d 2.8s 0.2s 0.0s 0.2s 0.4s 0.1s 1.8s
L1e 5.2s 0.3s 0.0s 0.3s 1.9s 0.5s 2.2s
L2a 5.7s 0.5s 0.1s 0.3s 1.8s 0.7s 2.2s
L2b 3.2s 0.1s 0.1s 0.2s 0.4s 0.1s 2.2s
L2c 5.8s 0.3s 0.2s 0.5s 1.5s 0.3s 3.1s
L2d 3.4s 0.7s 0.1s 0.3s 0.6s - 1.8s
L2e 2.8s 0.3s 0.2s 0.2s 0.3s 0.1s 1.9s
L1 = native French, L2 = native English
average time per input word
Philipp Koehn Computer Aided Translation 10 June 2010
32. 21
Time Spent on Activities
not much time Pauses
User total initial final short medium big keystroke
L1a 3.3s 0.1s 0.1s 0.2s 1.0s 0.1s 1.8s
L1b 7.7s 1.3s 0.1s 0.3s 1.8s 1.9s 2.3s
L1c 3.9s 0.2s 0.2s 0.3s 0.7s - 2.5s
L1d 2.8s 0.2s 0.0s 0.2s 0.4s 0.1s 1.8s
L1e 5.2s 0.3s 0.0s 0.3s 1.9s 0.5s 2.2s
L2a 5.7s 0.5s 0.1s 0.3s 1.8s 0.7s 2.2s
L2b 3.2s 0.1s 0.1s 0.2s 0.4s 0.1s 2.2s
L2c 5.8s 0.3s 0.2s 0.5s 1.5s 0.3s 3.1s
L2d 3.4s 0.7s 0.1s 0.3s 0.6s - 1.8s
L2e 2.8s 0.3s 0.2s 0.2s 0.3s 0.1s 1.9s
L1 = native French, L2 = native English
average time per input word
Philipp Koehn Computer Aided Translation 10 June 2010
33. 22
Time Spent on Activities
not much time Pauses similar
User total initial final short medium big keystroke
L1a 3.3s 0.1s 0.1s 0.2s 1.0s 0.1s 1.8s
L1b 7.7s 1.3s 0.1s 0.3s 1.8s 1.9s 2.3s
L1c 3.9s 0.2s 0.2s 0.3s 0.7s - 2.5s
L1d 2.8s 0.2s 0.0s 0.2s 0.4s 0.1s 1.8s
L1e 5.2s 0.3s 0.0s 0.3s 1.9s 0.5s 2.2s
L2a 5.7s 0.5s 0.1s 0.3s 1.8s 0.7s 2.2s
L2b 3.2s 0.1s 0.1s 0.2s 0.4s 0.1s 2.2s
L2c 5.8s 0.3s 0.2s 0.5s 1.5s 0.3s 3.1s
L2d 3.4s 0.7s 0.1s 0.3s 0.6s - 1.8s
L2e 2.8s 0.3s 0.2s 0.2s 0.3s 0.1s 1.9s
L1 = native French, L2 = native English
average time per input word
Philipp Koehn Computer Aided Translation 10 June 2010
34. 23
Time Spent on Activities
not much time Pauses differences similar
User total initial final short medium big keystroke
L1a 3.3s 0.1s 0.1s 0.2s 1.0s 0.1s 1.8s
L1b 7.7s 1.3s 0.1s 0.3s 1.8s 1.9s 2.3s
L1c 3.9s 0.2s 0.2s 0.3s 0.7s - 2.5s
L1d 2.8s 0.2s 0.0s 0.2s 0.4s 0.1s 1.8s
L1e 5.2s 0.3s 0.0s 0.3s 1.9s 0.5s 2.2s
L2a 5.7s 0.5s 0.1s 0.3s 1.8s 0.7s 2.2s
L2b 3.2s 0.1s 0.1s 0.2s 0.4s 0.1s 2.2s
L2c 5.8s 0.3s 0.2s 0.5s 1.5s 0.3s 3.1s
L2d 3.4s 0.7s 0.1s 0.3s 0.6s - 1.8s
L2e 2.8s 0.3s 0.2s 0.2s 0.3s 0.1s 1.9s
L1 = native French, L2 = native English
average time per input word
Philipp Koehn Computer Aided Translation 10 June 2010
35. 24
Overview
• Volunteer Translation Projects
• Machine Translation
• Human Translation
• Assistance to Human Translators
• User Study 1
• User Study 2
Philipp Koehn Computer Aided Translation 10 June 2010
36. Related Work: Tools used by Translators25
• Translators often use standard text editors and additional tools
• Bilingual dictionary
• Spell checker, grammar checker
• Monolingual concordancer
• Terminology database
• Web search to establish and verify meaning of terms
Philipp Koehn Computer Aided Translation 10 June 2010
37. 26
Translation Memory
• Source:
This feature is available for free in the QX 3400.
• Fuzzy match in translation memory:
This feature is available for free in the QX 3200.
Diese Funktion ist kostenlos im Modell QX 3200 verf¨gbar.
u
• Translator inspects the fuzzy match and uses it in her translation.
Philipp Koehn Computer Aided Translation 10 June 2010
38. 27
Bilingual Concordancer
show translations in context (www.linguee.com)
Philipp Koehn Computer Aided Translation 10 June 2010
39. 28
Our Types of Assistance
• Sentence completion
– tool suggests how to complete the translation
– one phrase at a time
40. 28
Our Types of Assistance
• Sentence completion
– tool suggests how to complete the translation
– one phrase at a time
• Translation options
– most likely translations for each word and phrase
– ordered and color-highlighted by probability
41. 28
Our Types of Assistance
• Sentence completion
– tool suggests how to complete the translation
– one phrase at a time
• Translation options
– most likely translations for each word and phrase
– ordered and color-highlighted by probability
• Postediting machine translation
– start with machine translation output
– user edits, tool shows changes
Philipp Koehn Computer Aided Translation 10 June 2010
42. 29
Technical Notes
• Online at http://www.caitra.org/
• User uploads source text, translates one sentence at a time
• Implementation
– AJAX Web 2.0 using Ruby on Rails, mySQL
– Back end: Moses machine translation system
Philipp Koehn Computer Aided Translation 10 June 2010
43. 30
Predicting Sentence Completion
• Tool makes a suggestion how to continue (in red)
44. 30
Predicting Sentence Completion
• Tool makes a suggestion how to continue (in red)
• User can accept it (by pressing tab), or type in her own translation
45. 30
Predicting Sentence Completion
• Tool makes a suggestion how to continue (in red)
• User can accept it (by pressing tab), or type in her own translation
• Same idea as TransType, with minor modifications
– show only short text chunks, not full sentence completion
– show only one suggestion, not alternatives
Philipp Koehn Computer Aided Translation 10 June 2010
46. 31
How does it work?
• Uses search graph of SMT decoding
47. 31
How does it work?
• Uses search graph of SMT decoding
• Matches partial user translation against search graph, by optimizing
1. minimal string edit distance between path in graph and user translation
2. best full path probability, including best completion to end
48. 31
How does it work?
• Uses search graph of SMT decoding
• Matches partial user translation against search graph, by optimizing
1. minimal string edit distance between path in graph and user translation
2. best full path probability, including best completion to end
• Technical notes
– search graph is pre-computed and stored in database
– matching is done server-side, typically takes less than 1 second
– completion path is returned to client (web brower)
Philipp Koehn Computer Aided Translation 10 June 2010
49. 32
Translation Options
• For each word and phrases: suggested translations
• Ranked (and color-highlighted) by probability
• User may click on suggestion → appended to text box
Philipp Koehn Computer Aided Translation 10 June 2010
50. Translation Options - How does it work?33
• Uses phrase translation table of SMT system
51. Translation Options - How does it work?33
• Uses phrase translation table of SMT system
• Translation score: future cost estimate
– e ¯ ¯e
conditional probabilities φ(¯|f ), φ(f |¯)
– e ¯ ¯e
lexical probabilities lex(¯|f ), lex(f |¯)
– word count feature
– language model estimate
52. Translation Options - How does it work?33
• Uses phrase translation table of SMT system
• Translation score: future cost estimate
– e ¯ ¯e
conditional probabilities φ(¯|f ), φ(f |¯)
– e ¯ ¯e
lexical probabilities lex(¯|f ), lex(f |¯)
– word count feature
– language model estimate
• Ranking of shorter vs. longer phrases by including outside future cost estimate
Philipp Koehn Computer Aided Translation 10 June 2010
53.
54. 35
Postediting Machine Translation
• Textbox is initially filled with machine translation
• User edits translation
• String edit distance to machine translation is shown (blue background)
Philipp Koehn Computer Aided Translation 10 June 2010
55. 36
Overview
• Volunteer Translation Projects
• Machine Translation
• Human Translation
• Assistance to Human Translators
• User Study 1
• User Study 2
Philipp Koehn Computer Aided Translation 10 June 2010
56. 37
Evaluation
• Recall setup
– 10 students, half native French, half native English
– each student translated French-English news stories
– about 40 sentences for each condition of assistance
57. 37
Evaluation
• Recall setup
– 10 students, half native French, half native English
– each student translated French-English news stories
– about 40 sentences for each condition of assistance
• Five different conditions
– unassisted
– prediction (sentence completion)
– options
– predictions and options
– post-editing
Philipp Koehn Computer Aided Translation 10 June 2010
58. 38
Quality
• We want faster translators, but not worse
• Assessment of translation quality
– show translations to bilingual judges, with source
– judgment: fully correct? yes/no
Indicate whether each user’s input represents a fully fluent and
meaning-equivalent translation of the source. The source is shown
with context, the actual sentence is bold.
59. 38
Quality
• We want faster translators, but not worse
• Assessment of translation quality
– show translations to bilingual judges, with source
– judgment: fully correct? yes/no
Indicate whether each user’s input represents a fully fluent and
meaning-equivalent translation of the source. The source is shown
with context, the actual sentence is bold.
• Average score: 50% correct — lower than expected
– judges seemed to be too harsh
– when given several translations, tendency to judge half as bad
Philipp Koehn Computer Aided Translation 10 June 2010
60. 39
Example of Quality Judgments
Src. Sans se d´monter, il s’est montr´ concis et pr´cis.
e e e
MT Without dismantle, it has been concise and accurate.
1/3 Without fail, he has been concise and accurate. (Prediction+Options, L2a)
4/0 Without getting flustered, he showed himself to be concise and precise. (Unassisted, L2b)
4/0 Without falling apart, he has shown himself to be concise and accurate. (Postedit, L2c)
1/3 Unswayable, he has shown himself to be concise and to the point. (Options, L2d)
0/4 Without showing off, he showed himself to be concise and precise. (Prediction, L2e)
1/3 Without dismantling himself, he presented himself consistent and precise.
(Prediction+Options, L1a)
2/2 He showed himself concise and precise. (Unassisted, L1b)
3/1 Nothing daunted, he has been concise and accurate. (Postedit, L1c)
3/1 Without losing face, he remained focused and specific. (Options, L1d)
3/1 Without becoming flustered, he showed himself concise and precise. (Prediction, L1e)
Philipp Koehn Computer Aided Translation 10 June 2010
63. 42
Slow Users 1: Faster and Better
8s
2b
7s • Unassisted
6s – more than 5 seconds per input word
1a – very bad (35%, 16%)
5s
+
E O
• With assistance
4s
– much faster and better
3s
P + P
– reaching roughly average performance
O
2s
E
1s
10% 20% 30% 40% 50% 60%
Philipp Koehn Computer Aided Translation 10 June 2010
64. 43
Slow Users 2: Only Faster
8s
7s • Unassisted
6s – more than 5 seconds per input word
1c
– average quality
2e
5s O
O +
4s P
• With assistance
E
P
– faster and but not better
3s E
+
2s
1s
30% 40% 50% 60%
Philipp Koehn Computer Aided Translation 10 June 2010
65. 44
Fast Users
4s 2c O
2a
3s P
+
2s E
+O
E
1s P
10% 20% 30% 40% 50% 60% 70% 80%
• Unassisted
– fast: 3-4 seconds per input word
– L1a is very bad (23%), L1c is average (50%)
• With assistance
– faster and better
– L1a closer to average (30-45%), L1c becomes very good (54-61%)
Philipp Koehn Computer Aided Translation 10 June 2010
66. 45
Refuseniks
4s
1d
1b
3s E
2d E 1e
E
2s E
1s
10% 20% 30% 40% 50% 60% 70% 80%
• Use the assistance sparingly or not at all, and see generally no gains
• The two best translators are in this group
• Postediting
– mixed on quality (2 better, 1 worse, 1 same), but all faster
– best translator (L2e, 68%) becomes much better (record 79%)
Philipp Koehn Computer Aided Translation 10 June 2010
67. 46
Learning Curve
users become better over time with assistance
Philipp Koehn Computer Aided Translation 10 June 2010
68. 47
User Feedback
• Q: In which of the five conditions did you think you were most accurate?
– predictions+options: 5 users
– options: 2 users
– prediction: 1 user
– postediting: 1 user
69. 47
User Feedback
• Q: In which of the five conditions did you think you were most accurate?
– predictions+options: 5 users
– options: 2 users
– prediction: 1 user
– postediting: 1 user
• Q: Rank the different types of assistance on a scale from 1 to 5, where1
indicates not at all and 5 indicates very helpful.
– prediction+options: 4.6
– prediction: 3.9
– options: 3.7
– postediting: 2.9
70. 47
User Feedback
• Q: In which of the five conditions did you think you were most accurate?
– predictions+options: 5 users
– options: 2 users
– prediction: 1 user
– postediting: 1 user
• Q: Rank the different types of assistance on a scale from 1 to 5, where1
indicates not at all and 5 indicates very helpful.
– prediction+options: 4.6
– prediction: 3.9
– options: 3.7
– postediting: 2.9
Philipp Koehn Computer Aided Translation 10 June 2010
71. User Feedback 48
• Q: In which of the five conditions did you think you were most accurate?
– predictions+options: 5 users
– options: 2 users
– prediction: 1 user
– postediting: 1 user
• Q: Rank the different types of assistance on a scale from 1 to 5, where1
indicates not at all and 5 indicates very helpful.
– prediction+options: 4.6
– prediction: 3.9
– options: 3.7
– postediting: 2.9
• Note: does not match empirical results
Philipp Koehn Computer Aided Translation 10 June 2010
72. 49
Summary
• Assistance made translators faster
– average speed improvement from 4.4s/word to 2.7-3.7s/word
– reduction of big pauses
– reduction of typing effort in post-editing
73. 49
Summary
• Assistance made translators faster
– average speed improvement from 4.4s/word to 2.7-3.7s/word
– reduction of big pauses
– reduction of typing effort in post-editing
• Assistance made translators better
– average judgment increased from 47% to 51-55% with help
– even good translators get better with postediting
74. 49
Summary
• Assistance made translators faster
– average speed improvement from 4.4s/word to 2.7-3.7s/word
– reduction of big pauses
– reduction of typing effort in post-editing
• Assistance made translators better
– average judgment increased from 47% to 51-55% with help
– even good translators get better with postediting
• Some good translators ignored the assistance
75. 49
Summary
• Assistance made translators faster
– average speed improvement from 4.4s/word to 2.7-3.7s/word
– reduction of big pauses
– reduction of typing effort in post-editing
• Assistance made translators better
– average judgment increased from 47% to 51-55% with help
– even good translators get better with postediting
• Some good translators ignored the assistance
• Fastest and (barely) best with postediting, but did not like it
Philipp Koehn Computer Aided Translation 10 June 2010
76. 50
Overview
• Volunteer Translation Projects
• Machine Translation
• Human Translation
• Assistance to Human Translators
• User Study 1
• User Study 2
Philipp Koehn Computer Aided Translation 10 June 2010
77. 51
Experiment
• Monolingual translators
– 10 students/staff at the University of Edinburgh
– none knew Arabic or Chinese
– have access to full stories at a time, may correct prior sentences
78. 51
Experiment
• Monolingual translators
– 10 students/staff at the University of Edinburgh
– none knew Arabic or Chinese
– have access to full stories at a time, may correct prior sentences
• Bilingual translators
– 3 of the 4 reference translations in NIST test set
• Remaining reference translation as truth
Philipp Koehn Computer Aided Translation 10 June 2010
79. 52
Stories
Story Headline Sent. Words
1: chi White House Pushes for Nuclear Inspectors to Be Sent as Soon 6 207
as Possible to Monitor North Korea’s Closure of Its Nuclear
Reactors
2: chi Torrential Rains Hit Western India, 43 People Dead 10 204
3: chi Research Shows a Link between Arrhythmia and Two Forms 7 247
of Genetic Variation
4: chi Veteran US Goalkeeper Keller May Retire after America’s Cup 10 367
5: ara Britain: Arrests in Several Cities and Explosion of Suspicious 7 224
Car
6: ara Ban Ki-Moon Withdraws His Report on the Sahara after 8 310
Controversy Surrounding Its Content
7: ara Pakistani Opposition Leaders Call on Musharraf to Resign. 11 312
8: ara Al-Maliki: Iraqi Forces Are Capable of Taking Over the 8 255
Security Dossier Any Time They Want
Philipp Koehn Computer Aided Translation 10 June 2010
80. 53
Results: Arabic
60
50
40
30
20
10
0
mono1 mono2 mono3 mono4 mono5 mono6 mono7 mono8 mono9 mono10
percentage of sentences judged as correct
Philipp Koehn Computer Aided Translation 10 June 2010
82. 55
Results: Arabic
80
70
60
50
40
30
20
10
0
bi1 bi2 bi3 mono1 mono2 mono3 mono4 mono5 mono6 mono7 mono8 mono9 mono10
best monolinguals as good as worst bilingual
Philipp Koehn Computer Aided Translation 10 June 2010
83. 56
Results: Arabic and Chinese
80
Arabic
Chinese
70
60
50
40
30
20
10
0
bi1 bi2 bi3 mono1 mono2 mono3 mono4 mono5 mono6 mono7 mono8 mono9 mono10
mostly worse performance for Chinese
Philipp Koehn Computer Aided Translation 10 June 2010
84. 57
Results per Story
80 Bilingual
Mono Post-Edit
70
60
50
40
30
20
10
0
Chinese Weather Chinese Sports Arabic Diplomacy Arabic Politics
Chinese Politics Chinese Science Arabic Terror Arabic Politics
performance differs widely per story
Philipp Koehn Computer Aided Translation 10 June 2010
85. 58
Results per Story
80 Bilingual
Mono Post-Edit
70
60
50
40
30
20
10
0
Chinese Weather Chinese Sports Arabic Diplomacy Arabic Politics
Chinese Politics Chinese Science Arabic Terror Arabic Politics
one story: monolinguals as good as bilinguals
Philipp Koehn Computer Aided Translation 10 June 2010
86. 59
Offering more assistance
• Progress in computer aided translation
87. 59
Offering more assistance
• Progress in computer aided translation
• Interactive machine translation (TransType)
– show prediction of sentence completion
– recompute when user types own translation
88. 59
Offering more assistance
• Progress in computer aided translation
• Interactive machine translation (TransType)
– show prediction of sentence completion
– recompute when user types own translation
• Alternative translations [Koehn and Haddow, 2009]
– display translation options from translation model
– ranked by translation score
Philipp Koehn Computer Aided Translation 10 June 2010
89. 60
Translation Options
up to 10 translations for each word / phrase
Philipp Koehn Computer Aided Translation 10 June 2010
90. 61
Translation Options
Philipp Koehn Computer Aided Translation 10 June 2010
91. 62
Results with Options
80 Bilingual
Mono Post-Edit
70 Mono Options
60
50
40
30
20
10
0
Chinese Weather Chinese Sports Arabic Diplomacy Arabic Politics
Chinese Politics Chinese Science Arabic Terror Arabic Politics
no big difference — once significantly better
Philipp Koehn Computer Aided Translation 10 June 2010
92. 63
Error Analysis
(a) Critical Judges
• Reference
Torrential Rains Hit Western India, 43 People Dead
• Bilingual translator
Heavy Rains Plague Western India Leaving 43 Dead
Philipp Koehn Computer Aided Translation 10 June 2010
93. 64
Error Analysis
(b) Mistakes by the professional translators
• Reference
Over just two days on the 29th and 30th, rainfall in Mumbai reached
243 mm.
• Bilingual translator
The rainfall in Mumbai had reached 243 cm over the two days of the
29th and 30th alone.
Philipp Koehn Computer Aided Translation 10 June 2010
94. 65
Error Analysis
(b2) Domain knowledge vs. language skills
• Bilingual translator
With Munchen-Gladbach falling to the German Bundesliga 2, ...
• Monolingual translator
The M¨nchengladbach team fell into the second German league, ...
o
Philipp Koehn Computer Aided Translation 10 June 2010
95. 66
Error Analysis
(c) Bad English by monolingual translators
• Monolingual translator
The western region of india heavy rain killed 43 people.
Philipp Koehn Computer Aided Translation 10 June 2010
96. 67
Error Analysis
(d) Mistranslated / untranslated name
• Reference
Johndroe said that the two leaders ...
• Machine translation
Strong zhuo, pointing out that the two presidents ...
• Monolingual translator
Qiang Zhuo pointed out that the two presidents ...
Philipp Koehn Computer Aided Translation 10 June 2010
97. 68
Error Analysis
(e) Wrong relationship between entities
• Machine translation
The colombian team for the match, and it is very likely that the united
states and kai in the americas cup final performance.
• Monolingual translator 6
The Colombian team and the United States are very likely to end up
in the Americas Cup as the final performance.
• Monolingual translator 8
The next match against Colombia is likely to be the United States’
and Keller’s final performance in the current Copa America.
Philipp Koehn Computer Aided Translation 10 June 2010
98. 69
Error Analysis
(f) Badly muddled machine translation
• Reference
In the current America’s cup, he has, just as before, been given an
important job to do by head coach Bradley, but he clearly cannot win
the match singlehanded. The US team, made up of ”young guards,”...
• Machine translation
He is still being head coach bradley appointed to important, it’s even
a fist ”, four young guards at the beginning of the ”, the united states
is...
Philipp Koehn Computer Aided Translation 10 June 2010
99. 70
Conclusions
• Main findings
– monolingual translators may be as good as bilinguals
100. 70
Conclusions
• Main findings
– monolingual translators may be as good as bilinguals
– widely different performance by translator / story
101. 70
Conclusions
• Main findings
– monolingual translators may be as good as bilinguals
– widely different performance by translator / story
– named entity translation critically important
102. 70
Conclusions
• Main findings
– monolingual translators may be as good as bilinguals
– widely different performance by translator / story
– named entity translation critically important
• Various human factors important
– domain knowledge
103. 70
Conclusions
• Main findings
– monolingual translators may be as good as bilinguals
– widely different performance by translator / story
– named entity translation critically important
• Various human factors important
– domain knowledge
– language skills
104. 70
Conclusions
• Main findings
– monolingual translators may be as good as bilinguals
– widely different performance by translator / story
– named entity translation critically important
• Various human factors important
– domain knowledge
– language skills
– effort
Philipp Koehn Computer Aided Translation 10 June 2010
105. 71
Outlook
• More assistance
– named entity transliteration
– word-level confidence measures
– show syntactic structure [Albrecht et al., 2009]
106. 71
Outlook
• More assistance
– named entity transliteration
– word-level confidence measures
– show syntactic structure [Albrecht et al., 2009]
Philipp Koehn Computer Aided Translation 10 June 2010
107. 72
Try it at home!
http://www.caitra.org/
questions?
Philipp Koehn Computer Aided Translation 10 June 2010
108. 73
Further Analysis
• How does the assistance change translator behaviour?
• How do translators utilize assistance?
• How is the translation produced?
Philipp Koehn Computer Aided Translation 10 June 2010
109. 74
Keystroke Log
black: keystroke, purple: deletion, grey: cursor move
red: sentence completion accept
orange: click on translation option
Analysis: Segment into periods of activity: typing, tabbing, clicking, pauses
one second before and after a keystroke is part of typing interval
Philipp Koehn Computer Aided Translation 10 June 2010
121. Origin of Characters: Native English L2e86
User: L2e key click tab mt
Postedit 20% - - 79%
Options 77% 22% - -
Prediction 61% - 38% -
Prediction+Options 100% - - -
Although hardly any time
spent on assistance,
fair amount of characters
produced by it
Philipp Koehn Computer Aided Translation 10 June 2010
122. 87
pPauses: French-Native User L1bp
Philipp Koehn Computer Aided Translation 10 June 2010
123. 88
pPauses: English-Native User L2ep
Philipp Koehn Computer Aided Translation 10 June 2010
124. 89
Outlook: More analysis
• What do translators think about when they are pausing?
125. 89
Outlook: More analysis
• What do translators think about when they are pausing?
• What are the hard problems?
– unknown words
– words without direct translation
– syntactic re-arrangement
126. 89
Outlook: More analysis
• What do translators think about when they are pausing?
• What are the hard problems?
– unknown words
– words without direct translation
– syntactic re-arrangement
• What do translators change in post-editing?
Philipp Koehn Computer Aided Translation 10 June 2010
127. 90
Outlook: More experiments
• Different types of users
– experienced professional translators
– volunteer / amateur
– no/little knowledge of source language
128. 90
Outlook: More experiments
• Different types of users
– experienced professional translators
– volunteer / amateur
– no/little knowledge of source language
• Different types of language pairs
– target-side morphology a problem
– large-scale reordering maybe a problem
129. 90
Outlook: More experiments
• Different types of users
– experienced professional translators
– volunteer / amateur
– no/little knowledge of source language
• Different types of language pairs
– target-side morphology a problem
– large-scale reordering maybe a problem
• Different types of translation tasks
– familiar content for translator?
– very similar to previously translated text?
130. 90
Outlook: More experiments
• Different types of users
– experienced professional translators
– volunteer / amateur
– no/little knowledge of source language
• Different types of language pairs
– target-side morphology a problem
– large-scale reordering maybe a problem
• Different types of translation tasks
– familiar content for translator?
– very similar to previously translated text?
Philipp Koehn Computer Aided Translation 10 June 2010
131. 91
Interactive Post-Editing?
• word alignment to source
• confidence estimation of likely faulty parts
• integration with translation memory
Philipp Koehn Computer Aided Translation 10 June 2010
132. 92
pPauses: Unassistedp
Philipp Koehn Computer Aided Translation 10 June 2010
133. 93
Pauses: Options
Philipp Koehn Computer Aided Translation 10 June 2010
134. 94
Pauses: Prediction of sentence completion
Philipp Koehn Computer Aided Translation 10 June 2010
135. 95
Pauses: Postediting
Philipp Koehn Computer Aided Translation 10 June 2010