0
Combining, Adapting and Reusing Bi-texts
between Related Languages:
Application to Statistical Machine Translation
Preslav...
2
Plan
• Part I
- Introduction to Statistical Machine Translation
• Part II
- Combining, Adapting and Reusing Bi-texts bet...
3
Statistical
Machine Translation
4
Statistical Machine Translation (SMT)
Reach Out to Asia (ROTA) has
announced its fifth Wheels
‘n’ Heels, Qatar’s largest...
5
SMT:
The Noisy Channel Model
6
Translation as Decoding
• 1947, Warren Weaver, Rockefeller Foundation:
One naturally wonders if the problem of
translati...
7
The Basic Components of an SMT System
Look for the best English translation
that both conveys the French meaning
and is ...
8
Components of an SMT System
• Language Model
- English text е  P(e)
o good English  high probability
o bad English  l...
9
Combining P(e) and P(f|e)
How do we translate to English
the Russian phrase “красный цветок”?
P(e) P(f|e) P(e).P(f|e)
a ...
10
SMT:
The Language Model P(e)
11
Language Model
•Goal: prefer “good” to “bad” English
- “good” ≠ grammatical
- “bad” ≈ unlikely
•Examples (grammaticalit...
12
Example:
Grammatical but Low-probability Text
Eye halve a spelling checker
It came with my pea sea
It plainly marks fou...
13
Language Model:
Learned from Monolingual Text
14
Bigram Language Model
First-order
Markov model
(approximation)
Chain rule
)...|(P)|(P)|(P)|(P)(P
)...|(P)|(P)|(P)|(P)(P...
15
Bigram Language Model
)(
)(
)(
)(
)|(P
1
1
1
1
1




 
 i
ii
w
ii
ii
ii
wC
wwC
wwC
wwC
ww
i
     

n
...
16
SMT:
The Translation Model P(f|e)
17
Modeling P(f|e) – Sentence Level
Batman did not fight any cat woman .
Бэтмен не вел бой с никакой женщиной кошкой .
• C...
18
Modeling P(f|e)
Batman did not fight any cat woman .
Бэтмен не вел бой с никакой женщиной кошкой .
• Broken into smalle...
19
IBM Model 4: Generation
(Brown et al., CL 1993)
Batman did not fight any cat woman .
Batman not fight fight any cat wom...
20
IBM Model 4: Generation
(Brown et al., CL 1993)
Batman did not fight any cat woman .
Batman not fight fight any cat wom...
21
Translation Model: Learned from a Bi-Text
Reach Out to Asia (ROTA) has
announced its fifth Wheels
‘n’ Heels, Qatar’s la...
22
100 Sentence Pairs
23
1000 Sentence Pairs
24
10,000 Sentences = 1 Book
25
100,000 Sentences = Stack of Books
26
1,000,000 Sentences = Shelf of Books
27
10 Million Sentences = Large Shelf of Books
28
The Large Data Trend Continues
29
Alignment Levels
- Document
- Paragraph
- Sentence
oGale & Church algorithm
- Words
oIBM models
30
Learning Word Alignments
Using Expectation Minimization (EM)
… красивые цветы … красивые красные цветы … красивые девуш...
31
Learning Word Alignments
Using Expectation Minimization (EM)
… красивые цветы … красивые красные цветы … красивые девуш...
32
Learning Word Alignments
Using Expectation Minimization (EM)
… красивые цветы … красивые красные цветы … красивые девуш...
33
Learning Word Alignments
Using Expectation Minimization (EM)
… красивые цветы … красивые красные цветы … красивые девуш...
34
Phrase-based
SMT
35
Phrase-Based SMT
• Sentence is broken into phrases
– Contiguous token sequences
– Not linguistic units
• Each phrase is...
36
Phrase-Based Translation
• Multiple words  Multiple words
• Models context
• Handles non-compositional phrases
• More ...
37
Phrase-Based SMT:
Sample
Bulgarian-English Phrases
38
Sample Phrases: главен
главни прокурори chief prosecutors
главни счетоводители chief accountants
главни архитекти chief...
39
Sample Phrases: както
• както физическа , така и психическа ||| both
physical and psychological
• както целият регион |...
40
Phrase-Based SMT:
Sample
Russian-Bulgarian Phrases
41
Sample Phrases: заявление
• заявление ||| молба ||| 0.25 0.166667 1 1 2.718
• заявление об ||| молба за ||| 1 0.0052469...
42
Sample Phrases: звонок, звук
• звонка ||| звънец ||| 1 1 0.4 0.5 2.718
• звонка ||| звънеца ||| 0.25 0.2 0.4 0.5 2.718
...
43
Sample Phrases: здание
• здание ||| здание ||| 1 1 0.4 0.4 2.718
• здание ||| зданието ||| 0.75 0.5 0.6 0.6 2.718
• зда...
44
Sample Phrases: здравствуй
• здравствуй ||| добро утро ||| 1 0.75 0.333 0.0625 2.718
• здравствуй ||| здравей ||| 1 1 0...
45
Sample Phrases: необычайное
• необычайное ||| необикновено ||| 0.176471 0.142857 0.75 0.75 2.718
• необычайное ||| необ...
46
SMT:
Evaluation
47
How MT Evaluation is NOT Done…
• Backtranslation
- A “mythical” example (Hutchins,1995)
o En: The spirit is willing, bu...
48
The BLEU Evaluation Metric
(Papineni et al., ACL 2002)
Reference (human) translation:
The U.S. island of Guam is
mainta...
49
BLEU: Multiple Reference Translations
Reference translation 1:
The U.S. island of Guam is maintaining
a high state of a...
50
Phrase-Based SMT:
Parameter Tuning
51
The Basic Model, Revisited
argmax P(e | f) =
e
argmax P(e) x P(f | e) / P(f)
e
argmax P(e) x P(f | e)
e
argmax P(e)2.4 ...
52
Maximum BLEU Training
(Och, ACL 2003)
Translation
System
(Automatic,
Trainable)
Translation
Quality
Evaluator
(Automati...
53
Statistical Phrase-Based Translation
1. Training:
1. P(e): n-gram language model
2. P(f|e):
1. Generate word alignments...
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Dr. Preslav Nakov — Combining, Adapting and Reusing Bi-texts between Related Languages — Application to Statistical Machine Translation — part 1

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Bilingual sentence-aligned parallel corpora, or bitexts, are a useful resource for solving many computational linguistics problems including part-of speech tagging, syntactic parsing, named entity recognition, word sense disambiguation, sentiment analysis, etc.; they are also a critical resource for some real-world applications such as statistical machine translation (SMT) and cross-language information retrieval. Unfortunately, building large bi-texts is hard, and thus most of the 6,500+ world languages remain resource-poor in bi-texts. However, many resource-poor languages are related to some resource-rich language, with whom they overlap in vocabulary and share cognates, which offers opportunities for using their bi-texts.

We explore various options for bi-text reuse: (i) direct combination of bi-texts, (ii) combination of models trained on such bi-texts, and (iii) a sophisticated combination of (i) and (ii).

We further explore the idea of generating bitexts for a resource-poor language by adapting a bi-text for a resource-rich language. We build a lattice of adaptation options for each word and phrase, and we then decode it using a language model for the resource-poor language. We compare word- and phrase-level adaptation, and we further make use of cross-language morphology. For the adaptation, we experiment with (a) a standard phrase-based SMT decoder, and (b) a specialized beam-search adaptation decoder.

Finally, we observe that for closely-related languages, many of the differences are at the subword level. Thus, we explore the idea of reducing translation to character-level transliteration. We further demonstrate the potential of combining word- and character-level models.

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Transcript of "Dr. Preslav Nakov — Combining, Adapting and Reusing Bi-texts between Related Languages — Application to Statistical Machine Translation — part 1"

  1. 1. Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation Preslav Nakov, Qatar Computing Research Institute (collaborators: Jorg Tiedemann, Pidong Wang, Hwee Tou Ng) Yandex seminar August 13, 2014, Moscow, Russia
  2. 2. 2 Plan • Part I - Introduction to Statistical Machine Translation • Part II - Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation • Part III - Further Discussion on SMT
  3. 3. 3 Statistical Machine Translation
  4. 4. 4 Statistical Machine Translation (SMT) Reach Out to Asia (ROTA) has announced its fifth Wheels ‘n’ Heels, Qatar’s largest annual community event, which will promote ROTA’s partnership with the Qatar Japan 2012 Committee. Held at the Museum of Islamic Art Park on 10 February, the event will celebrate 40 years of cordial relations between the two countries. Essa Al Mannai, ROTA Director, said: “A group of 40 Japanese students are traveling to Doha especially to take part in our event. SMT systems: - learn from human-generated translations - extract useful knowledge and build models - use the models to translate new sentences
  5. 5. 5 SMT: The Noisy Channel Model
  6. 6. 6 Translation as Decoding • 1947, Warren Weaver, Rockefeller Foundation: One naturally wonders if the problem of translation could conceivably be treated as a problem in cryptography. When I look at an article in Russian, I say: ‘This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.’ Example: – Это действительно написано по-английски . – This is really written in English .
  7. 7. 7 The Basic Components of an SMT System Look for the best English translation that both conveys the French meaning and is grammatical.
  8. 8. 8 Components of an SMT System • Language Model - English text е  P(e) o good English  high probability o bad English  low probability • Translation Model - Pair <f,e>  P(f|e) o <f,e> are translations  high probability o <f,e> are not translations  low probability • Decoder - Given P(e), P(f|e), and f we look for е that maximizes [P(e).P(f|e)]
  9. 9. 9 Combining P(e) and P(f|e) How do we translate to English the Russian phrase “красный цветок”? P(e) P(f|e) P(e).P(f|e) a flower red ↓ ↑ ↓ red flower a ↓ ↑ ↓ flower red a ↓ ↑ ↓ a red dog ↑ ↓ ↓ dog cat mouse ↓ ↓ ↓ ↓ a red flower ↑ ↑ ↑
  10. 10. 10 SMT: The Language Model P(e)
  11. 11. 11 Language Model •Goal: prefer “good” to “bad” English - “good” ≠ grammatical - “bad” ≈ unlikely •Examples (grammaticality): - I do not like strong tea.  good - I do not like powerful tea.  bad - I like strong tea not.  bad - Like not tea strong do I.  bad
  12. 12. 12 Example: Grammatical but Low-probability Text Eye halve a spelling checker It came with my pea sea It plainly marks four my revue Miss steaks eye kin knot sea. Eye strike a key and type a word And weight four it two say Weather eye am wrong oar write It shows me a strait a weigh. As soon as a mist ache is maid It nose bee fore two long And eye can put the error rite Its rare lea ever wrong. Eye have run this poem threw it I am shore your pleased two no Its letter perfect awl the weigh My checker tolled me sew. Торопыжка был голодный - проглотил утюг холодный.
  13. 13. 13 Language Model: Learned from Monolingual Text
  14. 14. 14 Bigram Language Model First-order Markov model (approximation) Chain rule )...|(P)|(P)|(P)|(P)(P )...|(P)|(P)|(P)|(P)(P )...(P)(P 453423121 432153214213121 21 wwwwwwwww wwwwwwwwwwwwwww wwwe n    Andrei Markov
  15. 15. 15 Bigram Language Model )( )( )( )( )|(P 1 1 1 1 1        i ii w ii ii ii wC wwC wwC wwC ww i        n i iin wwwPwww 2 1121 |P...P P(“I eat an apple …”) = P(I | <S>) . P(eat | I) . P(an | eat) . P(apple | an) …
  16. 16. 16 SMT: The Translation Model P(f|e)
  17. 17. 17 Modeling P(f|e) – Sentence Level Batman did not fight any cat woman . Бэтмен не вел бой с никакой женщиной кошкой . • Cannot be estimated directly
  18. 18. 18 Modeling P(f|e) Batman did not fight any cat woman . Бэтмен не вел бой с никакой женщиной кошкой . • Broken into smaller steps
  19. 19. 19 IBM Model 4: Generation (Brown et al., CL 1993) Batman did not fight any cat woman . Batman not fight fight any cat woman . Batman not fight fight NULL any cat woman . Бэтмен не вел бой с никакой кошкой женщиной . Бэтмен не вел бой с никакой женщиной кошкой . n(3|fight) P-NULL t(не|not) d(8|7) (Brown et al., CL 1993)
  20. 20. 20 IBM Model 4: Generation (Brown et al., CL 1993) Batman did not fight any cat woman . Batman not fight fight any cat woman . Batman not fight fight NULL any cat woman . Бэтмен не вел бой с никакой кошкой женщиной . Бэтмен не вел бой с никакой женщиной кошкой . n(3|fight) P-NULL t(не|not) d(8|7) • All these probabilities could be learned if word alignments were available. • We can learn word alignments using EM. (Brown et al., CL 1993)
  21. 21. 21 Translation Model: Learned from a Bi-Text Reach Out to Asia (ROTA) has announced its fifth Wheels ‘n’ Heels, Qatar’s largest annual community event, which will promote ROTA’s partnership with the Qatar Japan 2012 Committee. Held at the Museum of Islamic Art Park on 10 February, the event will celebrate 40 years of cordial relations between the two countries. Essa Al Mannai, ROTA Director, said: “A group of 40 Japanese students are traveling to Doha especially to take part in our event.
  22. 22. 22 100 Sentence Pairs
  23. 23. 23 1000 Sentence Pairs
  24. 24. 24 10,000 Sentences = 1 Book
  25. 25. 25 100,000 Sentences = Stack of Books
  26. 26. 26 1,000,000 Sentences = Shelf of Books
  27. 27. 27 10 Million Sentences = Large Shelf of Books
  28. 28. 28 The Large Data Trend Continues
  29. 29. 29 Alignment Levels - Document - Paragraph - Sentence oGale & Church algorithm - Words oIBM models
  30. 30. 30 Learning Word Alignments Using Expectation Minimization (EM) … красивые цветы … красивые красные цветы … красивые девушки … … beautiful flowers … beautiful red flowers … beautiful girls …
  31. 31. 31 Learning Word Alignments Using Expectation Minimization (EM) … красивые цветы … красивые красные цветы … красивые девушки … … beautiful flowers … beautiful red flowers … beautiful girls …
  32. 32. 32 Learning Word Alignments Using Expectation Minimization (EM) … красивые цветы … красивые красные цветы … красивые девушки … … beautiful flowers … beautiful red flowers … beautiful girls …
  33. 33. 33 Learning Word Alignments Using Expectation Minimization (EM) … красивые цветы … красивые красные цветы … красивые девушки … … beautiful flowers … beautiful red flowers … beautiful girls …
  34. 34. 34 Phrase-based SMT
  35. 35. 35 Phrase-Based SMT • Sentence is broken into phrases – Contiguous token sequences – Not linguistic units • Each phrase is translated in isolation • Translated phrases are reordered Batman has not fought a cat woman yet . Бэтмен пока не сражался с женщиной кошкой . (Koehn&al., HLT-NAACL 2003) (Koehn&al., HLT-NAACL 2003)
  36. 36. 36 Phrase-Based Translation • Multiple words  Multiple words • Models context • Handles non-compositional phrases • More data – longer phrases
  37. 37. 37 Phrase-Based SMT: Sample Bulgarian-English Phrases
  38. 38. 38 Sample Phrases: главен главни прокурори chief prosecutors главни счетоводители chief accountants главни архитекти chief architects главни щабове main staffs главни улици main streets главни методисти senior instructors главно предизвикателство major challenge
  39. 39. 39 Sample Phrases: както • както физическа , така и психическа ||| both physical and psychological • както целият регион ||| like the whole region • както те са определени ||| as defined • както и размера ||| as well as the size • както и предишните редовни доклади ||| in line with previous regular reports • както и по други ||| and in other
  40. 40. 40 Phrase-Based SMT: Sample Russian-Bulgarian Phrases
  41. 41. 41 Sample Phrases: заявление • заявление ||| молба ||| 0.25 0.166667 1 1 2.718 • заявление об ||| молба за ||| 1 0.00524692 1 0.53125 2.718 • заявление об образовании ||| молба за образуването ||| 1 0.005 ... • заявления ||| заявление ||| 1 1 0.5 0.666667 2.718 • заявления ||| заявление от ||| 1 0.500677 0.5 0.222222 2.718 • заявляю ||| заявявам ||| 0.333333 0.6 1 1 2.718
  42. 42. 42 Sample Phrases: звонок, звук • звонка ||| звънец ||| 1 1 0.4 0.5 2.718 • звонка ||| звънеца ||| 0.25 0.2 0.4 0.5 2.718 • звонка ||| на звънеца ||| 1 0.2 0.2 0.128199 2.718 • звонки ||| звънци ||| 0.4 0.4 1 1 2.718 • звонко ||| звънко ||| 0.333333 0.428571 1 1 2.718 • звонков ||| звънци ||| 0.4 0.4 1 1 2.718 • звонку ||| звънеца ||| 0.25 0.2 1 1 2.718 • звонок ||| звънеца ||| 0.375 0.3 0.375 0.3 2.718 • звонок ||| звънецът ||| 1 1 0.125 0.1 2.718 • звонок ||| иззвъня ||| 0.6 0.625 0.375 0.5 2.718 • звук ||| звук ||| 0.666667 0.666667 1 1 2.718 • звука ||| звук ||| 0.333333 0.333333 0.666667 0.4 2.718 • звука ||| звука ||| 1 0.666667 0.333333 0.4 2.718 • звуки ||| звуци ||| 1 1 1 1 2.718
  43. 43. 43 Sample Phrases: здание • здание ||| здание ||| 1 1 0.4 0.4 2.718 • здание ||| зданието ||| 0.75 0.5 0.6 0.6 2.718 • здания ||| зданието ||| 0.25 0.5 0.2 0.375 2.718 • здания ||| зданието на ||| 1 0.250861 0.4 0.140625 2.718 • здания ||| сградите ||| 1 1 0.2 0.25 2.718 • здания ||| сградите на ||| 1 0.500861 0.2 0.09375 2.718
  44. 44. 44 Sample Phrases: здравствуй • здравствуй ||| добро утро ||| 1 0.75 0.333 0.0625 2.718 • здравствуй ||| здравей ||| 1 1 0.666667 0.5 2.718 • здравствуйте ||| здравейте ||| 1 1 1 1 2.718 • здравствует ||| живее ||| 0.4 0.333333 1 1 2.718
  45. 45. 45 Sample Phrases: необычайное • необычайное ||| необикновено ||| 0.176471 0.142857 0.75 0.75 2.718 • необычайное ||| необикновеното ||| 0.333333 0.333333 0.25 0.25 2.718 • необычайно ||| извънредно ||| 1 0.4 0.125 0.117647 2.718 • необычайно ||| необикновена ||| 0.222222 0.166667 0.125 0.117647 2.718 • необычайно ||| необикновено ||| 0.588235 0.476191 0.625 0.588235 2.718 • необычайно ||| необичайно ||| 1 1 0.0625 0.117647 2.718 • необычайной ||| необикновена ||| 0.333333 0.416667 0.5 0.625 2.718 • необычайной ||| необикновено ||| 0.0588235 0.047619 0.166667 0.125 2.718 • необычайной ||| с необикновена ||| 1 0.209808 0.333333 0.15625 2.718 • необычайные ||| необикновени ||| 0.5 0.5 1 1 2.718 • необычайный ||| необикновен ||| 0.222222 0.222222 0.5 0.5 2.718 • необычайный ||| необикновеният ||| 0.5 0.5 0.25 0.25 2.718 • необычайный ||| необичайни ||| 0.333333 0.25 0.25 0.25 2.718 • необычное ||| необикновеното ||| 0.666667 0.666667 1 1 2.718 • необычные ||| необичайни ||| 0.666667 0.5 1 1 2.718 • неожиданной ||| неочакваната ||| 0.333333 0.333333 0.25 0.25 2.718 • неожиданной ||| неочаквана ||| 0.666667 0.6 0.75 0.75 2.718
  46. 46. 46 SMT: Evaluation
  47. 47. 47 How MT Evaluation is NOT Done… • Backtranslation - A “mythical” example (Hutchins,1995) o En: The spirit is willing, but the flesh is weak. o Ru: Дух бодр, но плоть слаба. o En. The vodka is good, but the meat is rotten. - Not used, can be gamed easily: o En: The spirit is willing, but the flesh is weak. o Ru: The spirit is willing, but the flesh is weak. o En: The spirit is willing, but the flesh is weak.
  48. 48. 48 The BLEU Evaluation Metric (Papineni et al., ACL 2002) Reference (human) translation: The U.S. island of Guam is maintaining a high state of alert after the Guam airport and its offices both received an e-mail from someone calling himself the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport . Machine translation: The American [?] international airport and its the office all receives one calls self the sand Arab rich business [?] and so on electronic mail , which sends out ; The threat will be able after public place and so on the airport to start the biochemistry attack , [?] highly alerts after the maintenance. • BLEU4 formula (counts n-grams up to length 4) exp (1.0 * log p1 + 0.5 * log p2 + 0.25 * log p3 + 0.125 * log p4 – max(words-in-reference / words-in-machine – 1, 0) p1 = 1-gram precision p2 = 2-gram precision p3 = 3-gram precision p4 = 4-gram precision  Correlates well with human judgments  Very hard to “game” it (Papineni et al., ACL 2002)
  49. 49. 49 BLEU: Multiple Reference Translations Reference translation 1: The U.S. island of Guam is maintaining a high state of alert after the Guam airport and its offices both received an e-mail from someone calling himself the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport . Reference translation 3: The US International Airport of Guam and its office has received an email from a self-claimed Arabian millionaire named Laden , which threatens to launch a biochemical attack on such public places as airport . Guam authority has been on alert . Reference translation 4: US Guam International Airport and its office received an email from Mr. Bin Laden and other rich businessman from Saudi Arabia . They said there would be biochemistry air raid to Guam Airport and other public places . Guam needs to be in high precaution about this matter . Reference translation 2: Guam International Airport and its offices are maintaining a high state of alert after receiving an e-mail that was from a person claiming to be the wealthy Saudi Arabian businessman Bin Laden and that threatened to launch a biological and chemical attack on the airport and other public places . Machine translation: The American [?] international airport and its the office all receives one calls self the sand Arab rich business [?] and so on electronic mail , which sends out ; The threat will be able after public place and so on the airport to start the biochemistry attack , [?] highly alerts after the maintenance. Reference translation 1: The U.S. island of Guam is maintaining a high state of alert after the Guam airport and its offices both received an e-mail from someone calling himself the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport . Reference translation 3: The US International Airport of Guam and its office has received an email from a self-claimed Arabian millionaire named Laden , which threatens to launch a biochemical attack on such public places as airport . Guam authority has been on alert . Reference translation 4: US Guam International Airport and its office received an email from Mr. Bin Laden and other rich businessman from Saudi Arabia . They said there would be biochemistry air raid to Guam Airport and other public places . Guam needs to be in high precaution about this matter . Reference translation 2: Guam International Airport and its offices are maintaining a high state of alert after receiving an e-mail that was from a person claiming to be the wealthy Saudi Arabian businessman Bin Laden and that threatened to launch a biological and chemical attack on the airport and other public places . Machine translation: The American [?] international airport and its the office all receives one calls self the sand Arab rich business [?] and so on electronic mail , which sends out ; The threat will be able after public place and so on the airport to start the biochemistry attack , [?] highly alerts after the maintenance. (Papineni et al., ACL 2002)
  50. 50. 50 Phrase-Based SMT: Parameter Tuning
  51. 51. 51 The Basic Model, Revisited argmax P(e | f) = e argmax P(e) x P(f | e) / P(f) e argmax P(e) x P(f | e) e argmax P(e)2.4 x P(f | e) e argmax P(e)2.4 x P(f | e) x #words(e)1.1 e Rewards longer hypotheses, since they are unfairly penalized by P(e) Works better x P(e | f)1.1 x Plex(f | e)1.3 x Plex(e | f)0.9 x #phrases(e,f)0.5... (Och, ACL 2003)
  52. 52. 52 Maximum BLEU Training (Och, ACL 2003) Translation System (Automatic, Trainable) Translation Quality Evaluator (Automatic) French input English MT Output English Reference Translations (sample “right answers”) BLEU score Language Model #1 Translation Model Language Model #2 Length Model Other Features MERT: Minimum Error Rate Training (optimizes BLEU directly) (Och, ACL 2003)
  53. 53. 53 Statistical Phrase-Based Translation 1. Training: 1. P(e): n-gram language model 2. P(f|e): 1. Generate word alignments 2. Build a phrase table 2. Tuning: 1. Use MERT to tune the parameters 3. Evaluation: 1. Run the system on test data 2. Calculate BLEU
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