Statistical Machine Translation

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1 Statistical Machine Translation Some of the content of this lecture is taken from previous lectures and presentations given by Philipp Koehn and Andy Way. Dr. Jennifer Foster National Centre for Language Technology School of Computing Dublin City University Week Ten/Eleven 17th April 2014

2 Lecture Plan Part One: Recap of decoding Part Two: More on decoding Part Three: Encoding linguistic knowledge

3 Part One Recap of decoding

4 Questions 1. What is decoding? 2. What methods are used to make the decoding problem tractable? 3. What is the difference between histogram pruning and threshold pruning?

5 What is decoding? Decoding is the process of searching for the most likely translation within the space of candidate translations.

6 Making decoding tractable 1. Hypothesis recombination 2. Pruning

7 Histogram Pruning Keep a maximum of m hypotheses

8 Threshold Pruning Keep only those hypotheses that are within a threshold α of the best hypothesis.

9 Histogram Pruning How many hypotheses will be pruned if m = 1?

10 Threshold Pruning How many hypotheses will be pruned if we prune all hypotheses that are at least 0.5 times worse than the best hypothesis? 0.09

11 Threshold Pruning 0.01 Threshold pruning is more flexible than histogram pruning since it takes into account the difference between the scores of the best and worst hypothesis

12 Part Two More on decoding

13 Stack Decoding Algorithm Partial translations or hypotheses are grouped into stacks based on how many words in the input sentence they translate.

14 Stack Decoding Algorithm Partial translations or hypotheses are grouped into stacks based on how many words in the input sentence they translate. Working left to right through the stacks, the decoder takes a hypothesis from a stack and a phrase from the input sentence and tries to construct a new hypothesis.

15 Stack Decoding Algorithm Partial translations or hypotheses are grouped into stacks based on how many words in the input sentence they translate. Working left to right through the stacks, the decoder takes a hypothesis from a stack and a phrase from the input sentence and tries to construct a new hypothesis. The output is the highest scoring hypothesis that translates the entire input sentence.

16 Stack Decoding Algorithm Partial translations or hypotheses are grouped into stacks based on how many words in the input sentence they translate. Working left to right through the stacks, the decoder takes a hypothesis from a stack and a phrase from the input sentence and tries to construct a new hypothesis. The output is the highest scoring hypothesis that translates the entire input sentence. Stacks are kept to a manageable size using pruning.

17 Stack Decoding Pseudocode 1:place empty hypothesis into stack 0 2: for all stacks 0...n 1 do 3: for all hypotheses in stack do 4: for all translation options do 5: if applicable then 6: create new hypothesis 7: place in stack 8: recombine with existing hypothesis if possible 9: prune stack if too big 10: end if 11: end for 12: end for 13: end for

18 Stack Decoding Example Input sentence: maison bleu Goal: Translate it into English using the stack decoding algorithm with histogram pruning (maximum number of hypotheses per stack = 3)

19 Stack Decoding Example Phrase pairs in training data: maison house maison home bleu blue bleu turquoise maison bleu blue house

20 Stack Decoding Example All possible translations: house blue, house turquoise, home blue, home turquoise, blue house, blue home, turquoise home, turquoise house

21 Stack Decoding Example Insert the empty hypothesis in Stack

22 Stack Decoding Example Construct new hypotheses compatible with empty hypothesis turquoise blue home house blue house 0 1 2

23 Stack Decoding Example Prune Stack 1 (assume turquoise has lowest probability) turquoise blue home house blue house 0 1 2

24 Stack Decoding Example Construct new hypotheses compatible with hypotheses in Stack 1 home turqoise house turquoise home blue blue home house house blue blue home blue house blue house 0 1 2

25 Stack Decoding Example Perform hypothesis recombination (assume in this case that the shorter path hypothesis is more likely) home turqoise house turquoise home blue blue home house house blue blue home blue house 0 1 2

26 Stack Decoding Example Prune stack 2 (assume home blue, home turquoise and house turquoise have the lowest scores) blue home house home turqoise housee turquoise home blue house blue blue home blue house 0 1 2

27 Stack Decoding Example Output most likely translation in Stack 2 blue house blue home blue home house blue house 0 1 2

28 Limitation of Stack Decoding Algorithm Relatively low-scoring hypotheses that are part of a good translation may be pruned.

29 Limitation of Stack Decoding Algorithm the tourism initiative addresses this for the first time Die Tm:-0.19, lm:-0.4, d:0,all:-0.59 touristische Tm:-1.16, lm:-2.93, d:0,all:-4.09 initiative Tm:-1.21, lm:-4.67, d:0,all:-5.88 das erste mal Tm:-0.56, lm:-2.81, d:-0.74,all:-4.11

30 Limitation of Stack Decoding Algorithm the tourism initiative addresses this for the first time Die Tm:-0.19, lm:-0.4, d:0,all:-0.59 touristische Tm:-1.16, lm:-2.93, d:0,all:-4.09 initiative Tm:-1.21, lm:-4.67, d:0,all:-5.88 das erste mal Tm:-0.56, lm:-2.81, d:-0.74,all:-4.11 Both hypotheses translate 3 words (therefore, in the same stack so will be compared to each other when deciding what to prune)

31 Limitation of Stack Decoding Algorithm the tourism initiative addresses this for the first time Die Tm:-0.19, lm:-0.4, d:0,all:-0.59 touristische Tm:-1.16, lm:-2.93, d:0,all:-4.09 initiative Tm:-1.21, lm:-4.67, d:0,all:-5.88 das erste mal Tm:-0.56, lm:-2.81, d:-0.74,all:-4.11 Both hypotheses translate 3 words (therefore, in the same stack so will be compared to each other when deciding what to prune) The worse hypothesis (i.e. the one starting with das erste mal) has a better score at this stage because it contains common words that are easier to translate.

32 Limitation of Stack Decoding Algorithm How to overcome this limitation?

33 Limitation of Stack Decoding Algorithm How to overcome this limitation? Estimate the future cost associated with a hypothesis by looking at the rest of the input string.

34 Future Cost A hypothesis score is some combination of its probability and its future cost. Why is the exact future cost not calculated?

35 Future Cost A hypothesis score is some combination of its probability and its future cost. Why is the exact future cost not calculated? Because calculating the exact future cost of all hypotheses would amount to evaluating the translation cost of all hypotheses the very thing we use pruning to avoid.

36 Depth-first versus breadth-first search A distinction is made between decoding algorithms that perform a breadth-first search and those that perform a depth-first search.

37 Depth-first versus breadth-first search A distinction is made between decoding algorithms that perform a breadth-first search and those that perform a depth-first search. A breadth-first search explores many hypotheses in parallel, e.g. stack decoding. A depth-first search fully explores a hypothesis before moving on to the next one, e.g. A* decoding.

38 Search and Model Errors 1. A search error occurs when the decoder misses a translation with a higher probability than the translation it returns.

39 Search and Model Errors 1. A search error occurs when the decoder misses a translation with a higher probability than the translation it returns. 2. A model error occurs when the model assigns a higher probability to an incorrect translation than to a correct one.

40 Part Three Making Use of Linguistic Knowledge

41 The story so far The SMT approaches we have studied so far are language-independent.

42 The story so far The SMT approaches we have studied so far are language-independent. Input: 1. A monolingual corpus for training a language model 2. A sentence-aligned parallel corpus for training a translation model

43 The story so far The SMT approaches we have studied so far are language-independent. Input: 1. A monolingual corpus for training a language model 2. A sentence-aligned parallel corpus for training a translation model Output: A translation (of variable quality)

44 The old way of doing things Traditional rule-based MT systems tried to translate using hand-coded rules which encode linguistic knowledge or generalisations.

45 Examples of linguistic knowledge 1. In English, the noun comes after the adjective. In French, the noun usually precedes the adjective.

46 Examples of linguistic knowledge 1. In English, the noun comes after the adjective. In French, the noun usually precedes the adjective. 2. In German, the main verb often occurs at the end of a clause.

47 Examples of linguistic knowledge 1. In English, the noun comes after the adjective. In French, the noun usually precedes the adjective. 2. In German, the main verb often occurs at the end of a clause. 3. In Irish, prepositions are inflected for number and person.

48 Examples of linguistic knowledge 1. In English, the noun comes after the adjective. In French, the noun usually precedes the adjective. 2. In German, the main verb often occurs at the end of a clause. 3. In Irish, prepositions are inflected for number and person. 4. In many languages, the subject and verb agree in person and number.

49 Why bother with linguistic knowledge? Why would we want to encode linguistic knowledge in an SMT system?

50 Why bother with linguistic knowledge? Why would we want to encode linguistic knowledge in an SMT system? 1. Data-driven SMT works well but there is room for improvement (especially for some language pairs).

51 Why bother with linguistic knowledge? Why would we want to encode linguistic knowledge in an SMT system? 1. Data-driven SMT works well but there is room for improvement (especially for some language pairs). 2. Data-driven SMT relies on the availability of large corpora. For minority languages, this can be problematic.

52 Using linguistic knowledge Where linguistic knowledge might be useful:

53 Using linguistic knowledge Where linguistic knowledge might be useful: 1. More global view of fluency than an n-gram language model: the house is the man is small

54 Using linguistic knowledge Where linguistic knowledge might be useful: 1. More global view of fluency than an n-gram language model: the house is the man is small 2. Long-distance dependencies: make a tremendous amount of sense

55 Using linguistic knowledge Where linguistic knowledge might be useful: 1. More global view of fluency than an n-gram language model: the house is the man is small 2. Long-distance dependencies: make a tremendous amount of sense 3. Word compounding: Knowing that Aktionsplan is made up of the words Aktion and plan

56 Using linguistic knowledge Where linguistic knowledge might be useful: 1. More global view of fluency than an n-gram language model: the house is the man is small 2. Long-distance dependencies: make a tremendous amount of sense 3. Word compounding: Knowing that Aktionsplan is made up of the words Aktion and plan 4. Better handling of function words: role of de in entreprises de transports (haulage companies)

57 How to encode linguistic knowledge How can linguistic knowledge be encoded in an SMT system?

58 How to encode linguistic knowledge How can linguistic knowledge be encoded in an SMT system? 1. Before translation (pre-processing)

59 How to encode linguistic knowledge How can linguistic knowledge be encoded in an SMT system? 1. Before translation (pre-processing) 2. During translation

60 How to encode linguistic knowledge How can linguistic knowledge be encoded in an SMT system? 1. Before translation (pre-processing) 2. During translation 3. After translation (post-processing)

61 Where do we get this linguistic knowledge? How do we obtain linguistic knowledge automatically?

62 Where do we get this linguistic knowledge? How do we obtain linguistic knowledge automatically? 1. Automatic morphological analysis 2. Part-of-speech tagging 3. Syntactic parsing

63 Where do we get this linguistic knowledge? How do we obtain linguistic knowledge automatically? 1. Automatic morphological analysis 2. Part-of-speech tagging 3. Syntactic parsing Can you spot a potential problem here (hint: automatic)?

64 Syntactic Parsing S NP VP dobj NNP VBP NNP John loves Mary NNP VBP NNP John loves Mary nsubj

65 Pre-processing Encoding linguistic knowledge before translation 1. Word segmentation 2. Word lemmatisation 3. Re-ordering

66 Word Segmentation The notion of what a word is varies from language to language.

67 Word Segmentation The notion of what a word is varies from language to language. In some languages, e.g. Chinese, words are not separated by spaces.

68 Word Segmentation The notion of what a word is varies from language to language. In some languages, e.g. Chinese, words are not separated by spaces. Why is this a problem for machine translation?

69 Word Segmentation The notion of what a word is varies from language to language. In some languages, e.g. Chinese, words are not separated by spaces. Why is this a problem for machine translation? Potential solution: try to perform automatic word segmentation before translation.

70 Word Segmentation The notion of what a word is varies from language to language. In some languages, e.g. Chinese, words are not separated by spaces. Why is this a problem for machine translation? Potential solution: try to perform automatic word segmentation before translation. A related challenge is word compounding in German.

71 Word Lemmatisation Languages with a rich inflectional system can have several different variants of the same root form or lemma.

72 Word Lemmatisation Languages with a rich inflectional system can have several different variants of the same root form or lemma. Example: Latin: bellum, belli, bello, bella, bellis, bellorum English: war, wars

73 Word Lemmatisation Languages with a rich inflectional system can have several different variants of the same root form or lemma. Example: Latin: bellum, belli, bello, bella, bellis, bellorum English: war, wars Why is this a problem for translation from an MRL (morphologically rich language) into English?

74 Word Lemmatisation Languages with a rich inflectional system can have several different variants of the same root form or lemma. Example: Latin: bellum, belli, bello, bella, bellis, bellorum English: war, wars Why is this a problem for translation from an MRL (morphologically rich language) into English? Potential solution: before translation, replace full form with lemma

75 Word Lemmatisation Languages with a rich inflectional system can have several different variants of the same root form or lemma. Example: Latin: bellum, belli, bello, bella, bellis, bellorum English: war, wars Why is this a problem for translation from an MRL (morphologically rich language) into English? Potential solution: before translation, replace full form with lemma

76 Syntactic Re-ordering It is harder to translate language pairs with very different word order.

77 Syntactic Re-ordering It is harder to translate language pairs with very different word order. Language model and phrase-based translation model can only do so much.

78 Syntactic Re-ordering It is harder to translate language pairs with very different word order. Language model and phrase-based translation model can only do so much. Potential solution: transform the source sentence so that its word order more closely resembles the word order of the target language.

79 Syntactic Re-ordering It is harder to translate language pairs with very different word order. Language model and phrase-based translation model can only do so much. Potential solution: transform the source sentence so that its word order more closely resembles the word order of the target language. Consider translating from English to German: I walked to the shop yesterday. I yesterday to the shop walked.

80 During Translation Encoding linguistic knowledge during translation

81 During Translation Encoding linguistic knowledge during translation 1. Log-linear models

82 During Translation Encoding linguistic knowledge during translation 1. Log-linear models 2. Tree-based models

83 During Translation Encoding linguistic knowledge during translation 1. Log-linear models 2. Tree-based models 3. Syntactic language models

84 Log-linear models The log-linear model of translation allows several sources of information to be employed simultaneously during the translation process: p x = exp n i=1 λ i h i (x)

85 Log-linear models The log-linear model of translation allows several sources of information to be employed simultaneously during the translation process: p x = exp n i=1 λ i h i (x) Some of this information can be linguistic knowledge, e.g. instead of just calculating p(ocras hungry) we can also calculate p(noun adj).

86 Tree-based models A more radical approach is to replace the phrasebased translation model with a different type of translation model entirely.

87 Tree-based models A more radical approach is to replace the phrasebased translation model with a different type of translation model entirely. One such model is a tree-based model which is based on the notion of a syntax tree and which allows one, in theory, to better capture structural similarities and divergences between languages.

88 Tree-based models A more radical approach is to replace the phrasebased translation model with a different type of translation model entirely. One such model is a tree-based model which is based on the notion of a syntax tree and which allows one, in theory, to better capture structural similarities and divergences between languages. During decoding, syntax trees for the source and language pairs are built up simultaneously (or one side only)

89 Syntactic language models N-gram language models are not the only language models.

90 Syntactic language models N-gram language models are not the only language models. People have experimented with other kinds of language models, including those based on the notion of a syntax tree.

91 Syntactic language models N-gram language models are not the only language models. People have experimented with other kinds of language models, including those based on the notion of a syntax tree. Instead of computing the probability of a sentence by multiplying n-gram probabilities, the probability is computed by multiplying the probabilities of the rules that are used to build the syntax tree(s).

92 Post-processing SMT systems return a ranked list of candidate translations. Linguistic information (of all kinds) can be employed in re-ranking this list using machine learning techniques.

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