1. 1 Gholamreza Haffari Simon Fraser University MT Summit, August 2009 Machine Learning approaches for dealing with Limited Bilingual Data in SMT

2. 2 Acknowledgments  Special thanks to: Anoop Sarkar  Some slides are adapted or used from  Chris Callison Burch  Trevor Cohn  Dragos Stefan Munteanu

3. 3 Statistical Machine Translation  Translate from a source language to a target language by computer using a statistical model  M F  E is a standard log-linear model MFEMFE Source Lang. F Target Lang. E

4. 4 Log-Linear Models Feature functions Weights  In the test time, the best output t* for a given s is chosen by t * = arg max t  i w i. f i (t,s)

5. 5 Phrase-based SMT  M F  E is composed of two main components:  The language model f lm : Takes care of the fluency of the generated translation  The phrase table f pt : Takes care of the content of the source sentence in the generated translation Huge bitext is needed to learn a high quality phrase dictionary

6. 6 Bilingual Parallel Data Source TextTarget Text

7. 7 This Talk What if we don’t have large bilingual text to learn a good phrase table?

8. 8 Motivations  Low-density Language pairs  Population speaking the language is small / Limited online resources  Adapting to a new style/domain/topic  Overcome training and testing mismatch

9. 9 Available Resources  Small bilingual parallel corpora  Large amounts of monolingual data  Comparable corpora  Small translation dictionary  Multilingual parallel corpora which includes multiple source languages but not the target language

10. 10 The Map source-target small bitext MT system large comparable source-target bitext parallel sentence extraction bilingual dictionary induction large source monotext semi-supervised/ active learning source-another language bitext paraphrasing source-another another-target source-target bitexts triangulation/ co-training

11. 11 Learning Problems (I)  Supervised Learning:  Given a sample of object-label pairs (x i,y i ), find the predictive relationship between object and labels  Un-supervised learning:  Given a sample consisting of only objects, look for interesting structures in the data, and group similar objects

12. 12 Learning Problems (II)  Now consider a training data consisting of:  Labeled data: Object-label pairs (x i,y i )  Unlabeled data: Objects x j  Leads to the following learning scenarios:  Semi-Supervised Learning: Find the best mapping from objects to labels benefiting from Unlabeled data  Transductive Learning: Find the labels of unlabeled data  Active Learning: Find the mapping while actively query the oracle for the label of unlabeled data

13. 13 The Big Picture Unlabeled {x j } (monotext) Labeled {(x i,y i )} (bitext) Data Train M Select Self-Training

14. 14 Mining More Bilingual Parallel Data  Comparable Corpora are texts which are not parallel in the strict sense but convey overlapping information  Wikipedia pages  New agencies: BBC, CNN  From comparable corpora, we can extract sentence pairs which are (approximately) translation of each other

15. 15 Extracting Parallel Sentences (Munteanu & Marcu, 2005) Un-matched Documents Parallel sentences

16. 16 Article Selection (Munteanu & Marcu, 2005)  Select the n-most relevant target-language docs to a source-language document using an information retrieval (IR) system:  Translate each source-lang article into a target-lang query using the bilingual dictionary Un-matched Documents

17. 17 Candidate Sentence Pair Selection (Munteanu & Marcu, 2005)  Consider all of the sentence pairs from the source- lang article and relevant target-lang articles. Filter the sentence pairs if:  The ratio of the length is more than 2  At least half of the words in each sentence does not have a translation in the other sentence

18. 18 Parallel Sentence Selection (Munteanu & Marcu, 2005)  Each candidate sentence pair (s,t) is classified into c 0 =‘parallel’ or c 1 =‘not parallel’ according to the following log-linear model:  The weights are learned during training phase using training data

19. 19 Model Features & Training Data (Munteanu & Marcu, 2005)  The features of the log-linear classifier include:  Length of the sentences, as well as their ratio  Percentage of words in one side that do not have translation in the other side / are not connected by alignment links  Training data can be prepared by a parallel corpus containing K sentence pairs  This gives K positive and K 2 – K negative examples (which can be filtered further using the previous heuristics)

20. 20 Improvement in SMT (Arabic to English) (Munteanu & Marcu, 2005) Initial out-of-domain parallel corpus Initial + extracted corpus Initial + human translated data

21. 21 Outline  Introduction  Semi-supervised Learning for SMT  Background (EM, Self-training, Co-Training)  SSL for Alignments / Phrases / Sentences  Active Learning for SMT  Single-language pair  Multiple Language Pairs

22. 22 Inductive vs.Transductive  Transductive: Produce label only for the available unlabeled data.  The output of the method is not a classifier  It’s like writing answers for the take-home exam!  Inductive: Not only produce label for unlabeled data, but also produce a classifier.  It’s like preparation for writing answers for the in-class exam!

23. 23 Self-Training Iteration: 0 + - A Model trained by SL Choose instances labeled with high confidence Iteration: 1 + - Add them to the pool of current labeled training data …… Iteration: 2 + - (Yarowsky 1995)

24. 24 EM  Use EM to maximize the joint log-likelihood of labeled and unlabeled data: : Log-likelihood of labeled data : Log-likelihood of unlabeled data (Dempster et al 1977)

25. 25 EM Iteration: 0 + - A Model trained by SL Clone new weighted labeled instances with unlab instances using (probabilisitc) model Iteration: 1 + - …… (Yarowsky 1995) w+iw+i w-iw-i Iteration: 2 + -

26. 26 Co-Training  Instances contain two sufficient sets of features  i.e. an instance is x=(x 1,x 2 )  Each set of features is called a View  Two views are independent given the label:  Two views are consistent: x x1x1 x2x2 (Blum & Mitchell 1998)

27. 27 Co-Training Iteration: t + - Iteration: t +1 + - …… C1: A Classifier trained on view 1 C2: A Classifier trained on view 2 Allow C1 to label Some instances Allow C2 to label Some instances Add self-labeled instances to the pool of training data

28. 28 Outline  Introduction  Semi-supervised Learning for SMT  Background (EM, Self-training, Co-Training)  SSL for Alignments / Phrases / Sentences  Active Learning for SMT  Single-language pair  Multiple Language Pairs

29. 29 Word Alignment & Translation Quality  (Fraser & Marcu 2006a) presented an SSL method for learning a better word alignment  A small / big set of sentence pairs annotated/unannotated with word alignments (~ 100 / ~ 2-3 million)  They showed that improvement in the word alignment caused improvement in the BLEU  The same conclusion was made later in (Ganchev et al 2008) for other translation tasks

30. 30 Word Alignment Model  Consider the following log-linear model for word alignment:  The feature functions are sub-models used in the IBM model 4, such as  Translation probability t(f|e)  Fertility probs n(  |e): number of words  generated by e ……

31. 31 SS-Word Alignment  (Fraser & Marcu 2006a) tuned the word alignment model parameters on the small labeled data in a discriminative fashion  With the current, generate the n-best list  Manipulate so that the best alignment stands out, i.e. the one which maximizes modified f-measure (MERT style alg)  Use to find the word alignments of the big unlabeled data  Estimate the feature functions’ parameters based on these best (Viterbi) alignments: 1 iteration of the EM algorithm  Repeat the above two steps

32. 32 Outline  Introduction  Semi-supervised Learning for SMT  Background (EM, Self-training, Co-Training)  SSL for Alignments / Phrases / Sentences  Active Learning for SMT  Single-language pair  Multiple Language Pairs

33. 33 Paraphrasing  If a word is unseen then SMT will not be able to translate it  Keep/omit/transliterate source word or use regular expression to translate it (dates, …)  If a phrase is unseen, but its individual words are, then SMT will be less likely to produce a correct translation  The idea: Use paraphrases in the source language to replace unknown words/phrases  Paraphrases are alternative ways of conveying the same information (Callison Burch, 2007)

34. 34 Coverage Problem in SMT Percentage of Test Item Types vs Corpus Size (Callison Burch, 2007)

35. 35 Behavior on Unseen Data  A system trained on 10,000 sentences (~200,000 words) may translate: Es positivo llegar a un acuerdo sobre los procedimientos, pero debemos encargarnos de que este sistema no sea susceptible de ser usado como arma pol´ıtica. as It is good reach an agreement on procedures, but we must encargarnos that this system is not susceptible to be usado as political weapon.  Since the translations of encargarnos and usado were not learned, they are either reproduced in the translation, or omitted entirely (Callison Burch, 2007)

36. 36 Substituting Paraphrases then Translating It is good reach an agreement on procedures, but we must encargarnos that this system is not susceptible to be usado as political weapon. encargarnos? usado? (Callison Burch, 2007)

37. 37 Substituting Paraphrases then Translating It is good reach an agreement on procedures, but we must encargarnos that this system is not susceptible to be usado as political weapon. encargarnos? garantizar velar procurar Asegurarnos usado? utilizado empleado uso utiliza (Callison Burch, 2007)

38. 38 Substituting Paraphrases then Translating It is good reach an agreement on procedures, but we must guarantee that this system is not susceptible to be used as political weapon. encargarnos? garantizar velar procurar Asegurarnos guarantee, ensure, guaranteed, assure, provided ensure, ensuring, safeguard, making sure ensure that, try to, ensure, endeavour to ensure, secure, make certain usado? utilizado empleado uso utiliza used, use, spent, utilized used, spent, employee use, used, usage used, uses, used, being used (Callison Burch, 2007)

39. 39 Learning paraphrases (I)  From monolingual parallel corpora  Multiple source sentences which are conveying the same information  Extract paraphrases seen in the same context in the aligned source sentences Emma burst into tears and he tried to comfort her, saying things to make her smile. Emma cried, and he tried to console her, adorning his words with puns. (Callison Burch, 2007)

40. 40 Learning paraphrases (I)  From monolingual parallel corpora  Multiple source sentences which are conveying the same information  Extract paraphrases seen in the same context in the aligned source sentences burst into tears = cried comfort= console Emma burst into tears and he tried to comfort her, saying things to make her smile. Emma cried, and he tried to console her, adorning his words with puns. (Callison Burch, 2007)

41. 41 Learning paraphrases (I)  From monolingual parallel corpora  Multiple source sentences which are conveying the same information  Extract paraphrases seen in the same context in the aligned source sentences  Problems with this approach  Monolingual parallel corpora are relatively uncommon  Limits what paraphrases we can generate, e.g. limited number of paraphrases (Callison Burch, 2007)

42. 42 Learning paraphrases (I)  From monolingual source corpora  For each unknown phrase x, build a distributional profile DP x which shows the co-occurrences of the surrounding words with x  Select the top-k phrases which have the most similar distributional profile with DP x  Is position important when building the profile? Should we simply count words, or use TF/IDF, or …? Which vector similarity measure should be used?  Needs smart tricks to make it scalable (Marton et al 2009)

43. 43 Learning paraphrases (II)  From bilingual parallel corpora  However no longer we have access to identical contexts  Adopt techniques from phrase-based SMT  Use aligned foreign language phrases as pivot (Callison Burch, 2007)

44. 44 Paraphrase Probability  Generate multiple paraphrases for a given phrase  We give them probabilities so they can be ranked  Define translation model probability:

45. 45 Refined Paraphrase Probability  Using multiple bilingual corpora, e.g. English-Spanish, English-German, …  C is the set of bilingual corpora and c is the weight of the corpus c, e.g. we may put more weight on larger corpora  Taking word sense into account  In a paraphrase, replace each word with its word_sense item

46. 46 Plugging Paraphrases into SMT Model  For each paraphrase s 2 having a translation t, we expand the phrase table by adding new entries (t,s 1 ) s 1  s 2  t  Add a new feature function into the SMT log-linear model to take into account the paraphrase probabilities p(s 2 | s 1 ) If phrase table entry (t,s 1 ) is generated from (t,s 2 ) 1 Otherwise f(t,s 1 ) =

47. 47 Results of Paraphrasing (Callison Burch, 2007)

48. 48 Improvement in Coverage (Callison Burch, 2007)

49. 49 Triangulation  We can find additional data by focusing on:  Multi-parallel corpora  Collection of bitexts with some common language(s) (Cohn & Lapata, 2007)

50. 50 Triangulation  We can find additional data by focusing on:  Multi-parallel corpora  Collection of bitexts with some common language(s) (Cohn & Lapata, 2007)

51. 51 Triangulation  We can find additional data by focusing on:  Multi-parallel corpora  Collection of bitexts with some common language(s) (Cohn & Lapata, 2007)

52. 52 Phrase-Level Triangulation  Triangulation (Kay, 1997)  Translate source phrase into an intermediate language phrase  Translate this intermediate phrase into the target phrase  Example: Translating a hot potato into French (Cohn & Lapata, 2007)

53. 53 A Generative Model for Triangulation  Marginalize out the intermediate phrases:  The generative model for p(s|t) : (Cohn & Lapata, 2007)

54. 54  Marginalize out the intermediate phrases:  Conditional independence assumption: i fully represents the information in t needed to translating s  Extends trivially to many intermediate languages  p(s|i) and p(i|t) are estimated using phrase frequencies (Cohn & Lapata, 2007) A Generative Model for Triangulation

55. 55 A Generative Model for Triangulation  Marginalize out the intermediate phrases:  Conditional independence may be violated  Translation model is estimated from noisy alignments  Missing contexts, i, in p(s|i)  Fewer large or rare phrases can be translated (Cohn & Lapata, 2007)

56. 56 Plugging Triangulated Phrases into Model  A mixture model of phrase pair probabilities from training set (standard) and the newly learned phrase pairs by triangulation:  As a new feature in the log-linear model standard triang + (1- )

57. 57 Coverage Benefit

58. 58 For any Language Pair?  10k bilingual sentences, interpolated with 3 intermediate langs: / (Cohn & Lapata, 2007)

59. 59 Larger Corpora  For French to English with Spanish as the intermediate language using different sizes for bitext(s)  triang: only triangulated phrases  interp: mixture model of the two phrase tables (Cohn & Lapata, 2007)

60. 60 What Languages are best for triangulation?  10K bilingual sentences, translating from French to English (Cohn & Lapata, 2007)

61. 61 How many languages are required?  10K bilingual sentences, translating from French to English, ordered by language family (Cohn & Lapata, 2007)

62. 62 Paraphrasing vs Triangulation  Paraphrasing  Uses bilingual projection to translate to and from a source phrase  It is employed to improve the source side coverage  Triangulation  Generalizes the paraphrasing method to any translation pathway linking the source and target  Improves both source and target coverage (Cohn & Lapata, 2007)

63. 63 Bilingual Lexicon Induction  The goal is to induce a larger bilingual dictionary. It can be used, for example, to augment the phrase table/parallel text  Suppose we have access to a small bilingual dictionary plus large monolingual text  Build distributional profile using use monolingual source text  Map the profile using seed rules (initial bilingual dictionary) to the target language vocabulary space  Select the top-k target language words with most similar distributional profiles (Rapp, 1999)

64. 64 Context-based Rapp Model ( Garera et al 2009)

65. 65 Dependency Context  Usually words in a fixed-size window are used to represent the context  (Garera et al 2009) uses the latent structure in the dependency parse tree to represent the context ( Garera et al 2009)

66. 66 Dependency Context  Usually words in a fixed-size window are used to represent the context  (Garera et al 2009) uses the latent structure in the dependency parse tree to represent the context  Dynamic context size  Accounts for reordering ( Garera et al 2009)

67. 67 Bilingual Lexicon Induction (more references)  (Koehn & Knight 2002) takes into account the orthographic features in addition to the context  (Haghighi et al 2008) devise a generative model which generates the (feature vector of) related words in the source and target languages  Each word is represented by a feature vector containing both contextual and ortographic features  (Mann & Yarowsky 2001) and (Schafer & Yarowsky 2002) use a bridge language to induce bilingual lexicon

68. 68 Bilingual Phrase Induction (non-comparable corpora)  Non-comparable corpora contain “... disparate, very nonparallel bilingual documents that could either be on the same topic (on-topic) or not” (Fung & Cheung 2004)  The goal is to extract parallel sub-sentential fragments, as opposed to extracting parallel sentences  Assume we have a lexical dictionary  P(t | s): the probability the source word s translates into target word t  Using some heuristics, specify the candidate sentence pairs ( Munteanu & Marcu 2006)

69. 69 The Signal Processing Approach target source

70. 70 The Signal Processing Approach target source

71. 71 The Signal Processing Approach target source

72. 72 The Signal Processing Approach P(t|s) target source

73. 73 The Signal Processing Approach target source

74. 74 The Signal Processing Approach target source

75. 75 The Signal Processing Approach target source Average of “signals” from neighbors

76. 76 The Signal Processing Approach target source Average of “signals” from neighbors

77. 77 Bilingual Phrase Induction (non-comparable corpora)  Retain “positive fragments”, i.e. those fragments for which the corresponding filtered signal values are positive  Repeat the procedure in the other direction (target to source) to obtain the fragments for source, and consider the resulting two text chunks as parallel  The signal filtering function is simple, more advanced filters might work better ( Munteanu & Marcu 2006)

78. 78 The Effect of Parallel Fragments for SMT ( Munteanu & Marcu 2006) Explained in the beginning of the talk The method just explained

79. 79 Outline  Introduction  Semi-supervised Learning for SMT  Background (EM, Self-training, Co-Training)  SSL for Alignments / Phrases / Sentences  Active Learning for SMT  Single-language pair  Multiple Language Pairs

80. 80 Self-Training for SMT Train MFEMFE Bilingual text F F E E Monolingual text Decode Translated text F F E E F F E E Select high quality Sent. pairs Select high quality Sent. pairs Re- Log-linear Model Re-training the SMT model

81. 81 Self-Training for SMT Train MFEMFE Bilingual text F F E E Monolingual text Decode Translated text F F E E F F E E Select high quality Sent. pairs Select high quality Sent. pairs Re- Log-linear Model Re-training the SMT model ( Ueffing et al 2007a )

82. 82 Scoring & Selecting Sentence Pairs  Scoring:  Use normalized decoder’s score  Confidence estimation method (Ueffing & Ney 2007)  Selecting:  Importance sampling:  Those whose score is above a threshold  Keep all sentence pairs

83. 83 Confidence Estimation  A log linear combination of  Word posterior probabilities: The chance of seeing a word in a particular position in translations  Phrase posterior probabilities  Language model score  The weights are tuned to minimize the classification error rate  Translations having a WER above a threshold are considered incorrect

84. 84 Self-Training for SMT Train MFEMFE Bilingual text F F E E Monolingual text Decode Translated text F F E E F F E E Select high quality Sent. pairs Select high quality Sent. pairs Re- Log-linear Model Re-training the SMT model ( Ueffing et al 2007a )

85. 85 Re-Training the SMT Model (I)  Simply add the newly selected sentence pairs to the initial bitext, and fully re-train the phrase table  A mixture model of phrase pair probabilities from training set combined with phrase pairs from the newly selected sentence pairs Initial Phrase TableNew Phrase Table + (1- ) ( Ueffing et al 2007a )

86. 86 Re-training the SMT Model (II)  Use new sentence pairs to train an additional phrase table and use it as a new feature function in the SMT log-linear model  One phrase table trained on sentences for which we have the true translations  One phrase table trained on sentences with their generated translations Phrase Table 1 Phrase Table 2

87. 87 Results (Chinese to English, Transductive) SelectionScoringBLEU%WER%PER% Baseline 27.9 .767.2 .644.0 .5 Keep all28.166.544.2 Importance Sampling Norm. score28.766.143.6 Confidence28.465.843.2 ThresholdNorm. score28.366.143.5 confidence29.365.643.2 WER: Lower is better (Word error rate) PER: Lower is better (Position independent WER ) BLEU: Higher is better Bold: best result, italic: significantly better Using additional phrase table

88. 88 Results (Chinese to English, Inductive) systemBLEU%WER%PER% Eval-04 (4 refs.) Baseline 31.8 .766.8 .741.5 .5 Add Chinese dataIter 132.865.740.9 Iter 432.665.840.9 Iter 1032.566.141.2 WER: Lower is better (Word error rate) PER: Lower is better (Position independent WER ) BLEU: Higher is better Bold: best result, italic: significantly better Using importance sampling and additional phrase table

89. 89 Why does it work (I)  Reinforces parts of the phrase translation model which are relevant for test corpus, hence obtain more focused probability distribution source | targetprob A B | a b e A B | c d ….5 … Decode monotext ---- A B ----- ---- c d ----- “c d” is chosen since LM picks it according to signals from context source | targetprob A B | a b e A B | c d ….2.8 … Use this to resolve ambiguity of translating “A B” in other parts of the text Retraining ( Ueffing et al 2008 )

90. 90 Why does it work (II)  Composes new phrases, for example: Original parallel corpusAdditional source dataPossible new phrases ‘A B’, ‘C D E’‘A B C D E’‘A B C’, ‘B C D E’, … Source: ----- A B C D E ----- Translation: ----- a b c d e ----- ----- A B C D E ----- ----- a b c d e ----- ----- A B C D E ----- ----- a b c d e ----- ( Ueffing et al 2008 )

91. 91 Analysis  New phrases are used rarely, hence most of the benefit comes from focused probability distributions

92. 92 Co-training for SMT  Source sentence is a view onto the translation  Existing translations of a source sentence can be used as additional views on the translation (Callison Burch, 2003)

93. 93 Co-Training for SMT (Callison Burch, 2003)

94. 94 Co-Training for SMT (Callison Burch, 2003) Having initial bitexts, train SMT models from source languages to the target language

95. 95 Co-Training for SMT (Callison Burch, 2003) Translate a multilingual parallel sentence in the source languages using the trained SMT models

96. 96 Co-Training for SMT (Callison Burch, 2003) Choose the best generated translation

97. 97 Co-Training for SMT (Callison Burch, 2003) Add the new sentence pairs to the bitexts and re-train the SMT models

98. 98 Results of Co-Training  20k initial labeled sentences, 60k unlabeled parallel sentences in 5 languages, select 10k pseudo-labeled sentences in each iteration (Callison Burch, 2003)

99. 99 Coaching  Suppose we have no German-English bitext  There is a French-English bitext  There is a French-German bitext  Train a French to English translation model  Translate the French to English and align the generated translations with German

100. 100 Results of Coaching Coaching of German to English by a French to English translation model (Callison Burch, 2003)

101. 101 Results of Coaching Coaching of German to English by multiple translation models (Callison Burch, 2003)

102. 102 Outline  Introduction  Semi-supervised Learning for SMT  Background (EM, Self-training, Co-Training)  SSL for Alignments / Phrases / Sentences  Active Learning for SMT  Single-language pair  Multiple Language Pairs

103. 103 Shortage of Bilingual Data: A Solution  Suppose we are given a large monolingual text in the source language F  Pay a human expert and ask him/her to translate these sentences into the target language E  This way, we will have a bigger bilingual text  But our budget is limited !  We cannot afford to translate all monolingual sentences

104. 104 A Better Solution  Choose a subset of monolingual sentences for which: if we had the translation, the SMT performance would increase the most  Only ask the human expert for the translation of these highly informative sentences  This is the goal of Active Learning

105. 105 Active Learning for SMT Train MFEMFE Bilingual text F F E E Monolingual text Decode Translated text F F E E Translate by human F F E E F F Select Informative Sentences Select Informative Sentences Re- Log-linear Model Re-training the SMT models (Haffari et al 2009)

106. 106 Active Learning for SMT Train MFEMFE Bilingual text F F E E Monolingual text Decode Translated text F F E E Translate by human F F E E F F Select Informative Sentences Select Informative Sentences Re- Log-linear Model Re-training the SMT models

107. 107 Sentence Selection Strategies  Baselines:  Randomly choose sentences from the pool of monolingual sentences  Choose longer sentences from the monolingual corpus  Other methods  Decoder’s confidence for the translations (Kato & Barnard, 2007)  Reverse model  Utility of the translation units (Haffari et al 2009)

108. 108 Decoder’s Confidence  Sentences for which the model is not confident about their translations are selected first  Hopefully high confident translations are good ones  Normalize the confidence score by the sentence length (Haffari et al 2009)

109. 109 Reverse Model Comparing  the original sentence, and  the final sentence Tells us something about the value of the sentence I will let you know about the issue later Je vais vous faire plus tard sur la question I will later on the question MEFMEF Rev: M F  E (Haffari et al 2009)

110. 110 Sentence Selection Strategies  Baselines:  Randomly choose sentences from the pool of monolingual sentences  Choose longer sentences from the monolingual corpus  Other methods  Decoder’s confidence for the translations (Kato & Barnard, 2007)  Reverse model  Utility of the translation units (Haffari et al 2009)

111. 111 Utility of the Translation Units Phrases are the basic units of translations in phrase-based SMT I will let you know about the issue later Monolingual Text 6 6 1 8 3 Bilingual Text 5 6 1 2 3 7 The more frequent a phrase is in the monolingual text, the more important it is The more frequent a phrase is in the bilingual text, the less important it is mm bb

112. 112 Generative Models for Phrases Monolingual TextBilingual Text 6 6 1 8 3 Count.25.05.33.12 Probability 5 6 1 2 3 7 CountProbability.21.22.05.09.14.29 mm bb

113. 113 Sentence Selection: Probability Ratio Score  For a monolingual sentence S  Consider the bag of its phrases:  Score of S depends on its probability ratio: = {,, }  m ( )  b ( )  m ( )  b ( )  m ( )  b ( )   (Haffari et al 2009)

114. 114 Sentence Selection: Probability Ratio Score  For a monolingual sentence S  Consider the bag of its phrases:  Score of S depends on its probability ratio:  Phrase probability ratio captures our intuition about the utility of the translation units = {,, } Phrase Prob. Ratio

115. 115 Extensions of the Score  Instead of using phrases, we may use n- grams  We may alternatively use the following score (Haffari et al 2009)

116. 116 Sentence Segmentation  How to prepare the bag of phrases for a sentence S?  For the bilingual text, we have the segmentation from the training phase of the SMT model  For the monolingual text, we run the SMT model to produce the top-n translations and segmentations  What about OOV fragments in the sentences of the monolingual text? (Haffari & Sarkar 2009)

117. 117 OOV Fragments: An Example i will go to school on friday OOV Fragment go toschoolon friday go to schoolon friday goto school onfriday OOV Phrases Which can be long (Haffari & Sarkar 2009b)

118. 118 Counting OOV Phrases  Fix an OOV fragment x  Put a uniform distribution over all possible segmentations of x  Use the expected count of OOV Phrases under this uniform distribution  See (Haffari & Sarkar 2009b) for how to compute these expectations efficiently x: … (Haffari & Sarkar 2009)

119. 119 Active Learning for SMT Train MFEMFE Bilingual text F F E E Monolingual text Decode Translated text F F E E Translate by human F F E E F F Select Informative Sentences Select Informative Sentences Re- Log-linear Model Re-training the SMT models

120. 120 Re-training the SMT Models  We use two phrase tables in each SMT model M Fi  E  One trained on sents for which we have the true translations  One trained on sents with their generated translations (Self-training) F i E i Phrase Table 1 Phrase Table 2

121. 121 Experimental Setup  Dataset size:  We select 200 sentences from the monolingual sentence set for 25 iterations  We use Portage from NRC as the underlying SMT system (Ueffing et al, 2007) BitextMonotexttest French-English5K20K2K

122. 122 The Simulated AL Setting Utility of phrases Random Decoder’s Confidence Better

123. 123 The Simulated AL Setting Better

124. 124 Outline  Introduction  Semi-supervised Learning for SMT  Background (EM, Self-training, Co-Training)  SSL for Alignments / Phrases / Sentences  Active Learning for SMT  Single-language pair  Multiple Language Pairs

125. 125 Multiple Language-Pair AL-SMT E (English)  Add a new lang. to a multilingual parallel corpus  To build high quality SMT systems from existing languages to the new lang. F 1 (German) F 2 (French) F 3 (Spanish) … AL Translation Quality

126. 126 AL-SMT: Multilingual Setting Train MFEMFE F 1,F 2, … E E Monolingual text Decode E 1,E 2,.. Translate by human Select Informative Sentences Select Informative Sentences Re- Log-linear Model Re-training the SMT models F 1,F 2, … E E

127. 127 Selecting Multilingual Sents. (I) Alternate Method: To choose informative sents. based on a specific F i in each AL iteration F 1 F 2 F 3 ……… 2 35 1 3 19 2 2 17 3 Rank (Reichart et al, 2008)

128. 128 Selecting Multilingual Sents. (II) Combined Method: To sort sents. based on their ranks in all lists F 1 F 2 F 3 ……… 2 35 1 3 19 2 2 17 3 Combined Rank … 7=2+3+2 71=35+19+17 6=1+2+3 (Reichart et al, 2008)

129. 129 Selecting Multilingual Sents. (III) Disagreement Method –Pairwise BLEU score of the generated translations –Sum of BLEU scores from a consensus translation F 1 F 2 F 3 ……… E 1 … E 2 … E 3 … Consensus Translation

130. 130 AL-SMT: Multilingual Setting Train MFEMFE F 1,F 2, … E E Monolingual text Decode E 1,E 2,.. Translate by human Select Informative Sentences Select Informative Sentences Re- Log-linear Model Re-training the SMT models F 1,F 2, … E E

131. 131 Re-training the SMT Models (I)  We use two phrase tables in each SMT model M Fi  E  One trained on sents for which we have the true translations  One trained on sents with their generated translations (Self-training) F i E i Phrase Table 1 Phrase Table 2

132. 132 Re-training the SMT Models (II)  Phrase Table 2: We can instead use the consensus translations (Co-Training) F i Phrase Table 1 E 1 E 2 E 3 E consensus Phrase Table 2

133. 133 Experimental Setup  We want to add English to a multilingual parallel corpus containing Germanic languages in EuroParl:  Germanic Langs: German, Dutch, Danish, Swedish  Sizes of dataset and selected sentences  Initially there are 5k multilingual sents parallel to English sents  20k parallel sents in multilingual corpora.  10 AL iterations, and select 500 sentences in each iteration  We use Portage from NRC as the underlying SMT system (Ueffing et al, 2007b)

134. 134 Self-training vs Co-training Germanic Langs to English Co-Training mode outperforms Self-Training mode 19.75 20.20

135. 135 Germanic Languages to English methodSelf-Training WER / PER / BLEU Co-Training WER / PER / BLEU Combined Rank Alternate Random WER: Lower is better (Word error rate) PER: Lower is better (Position independent WER ) BLEU: Higher is better 41.0 40.2 41.6 40.1 40.0 40.5 30.2 30.0 31.0 30.1 29.6 30.7 19.9 20.0 19.4 20.2 20.3 20.2 Bold: best result, italic: significantly better

136. 136 Conclusion source-target small bitext MT system large comparable source-target bitext parallel sentence extraction bilingual dictionary induction large source monotext semi-supervised/ active learning source-another language bitext paraphrasing source-another another-target source-target bitexts triangulation/ co-training

137. 137 Finish

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