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Direct MT, Example-based MT, Statistical MT

Direct MT, Example-based MT, Statistical MT

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Direct MT, Example-based MT, Statistical MT

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  1. Direct MT, Example-based MT, Statistical MT

  2. Issues in Machine Translation • Orthography • Writing from left-to-right vs right-to-left • Character sets (alphabetic, logograms, pictograms) • Segmentation into word/word-like units • Morphology • Lexical: Word senses • bank  “river bank”, “financial institution” • Syntactic: Word order • Subject-verb-object  subject-object-verb • Semantic: meaning • “ate pasta with a spoon”, “ate pasta with marinara”, “ate pasta with John” • Pragmatic: world knowledge • “Can you pass me the salt?” • Social: conversational norms • pronoun usage depends on the conversational partner • Cultural: idioms and phrases • “out of the ballpark”, “came from leftfield” • Contextual • In addition for Speech Translation • Prosody: JOHN eats bananas: John EATS bananas; John eats BANANAS • Pronunciation differences • Speech recognition errors • In a multilingual environment • Code Switching: Use of linguistic apparatus of one language to express ideas in another language.

  3. MT Approaches: Different levels of meaning transfer Interlingua Semantic Interpretation Semantic Generation Depth of Analysis Syntactic Structure Syntactic Structure Transfer-based MT Syntactic Generation Parsing Target Source Direct MT

  4. Direct Machine Translation • Words are replaced using a dictionary • Some amount of morphological processing • Word reordering is limited • Quality depends on the size of the dictionary, closeness of languages Spanish : ajá quiero usar mi tarjeta de crédito English : yeah I wanna use my credit card Alignment : 1 3 4 5 7 0 6 • English :I need to make a collect call • Japanese :私は コレクト コールを かける 必要があります • Alignment : 1 5 0 3 0 2 4

  5. Translation Memory • Idea is to reuse translations that were done in the past • Useful for technical terminology • Ideally used in a sub-language translation • System helps in matching new instances against previously translated instances • Choices are presented to a human translator through a GUI • Human translator selects and “stitches” the available options to cover the source language sentence • If no match is found, the translator introduces a new translation pair into the translation memory. • Pros: • Maintains consistency in translation across multiple translators • Improves efficiency of translation process • Issues: How is the matching done? • Word level matching, morphological root matching • Determines robustness of the translation memory

  6. ALIGNMENT (transfer) MATCHING (analysis) RECOMBINATION (generation) Exact match (direct translation) Target Source Example-based MT • Translation-by-analogy: • A collection of source/target text pairs • A matching metric • An word or phrase-level alignment • Method for recombination • ATR EBMT System (E. Sumita, H. Iida, 1991); CMU Pangloss EBMT (R. Brown, 1996)

  7. Example run of EBMT English-Japanese Examples in the Corpus: • He buys a notebook Kare wa noto o kau • I read a book on international politics Watashi wa kokusai seiji nitsuite kakareta hon o yomu Translation Input:He buysa book on international politics Translation Output:Kare wakokusai seiji nitsuite kakareta hono kau • Challenge: Finding a good matching metric • He bought a notebook • A book was bought • I read a book on world politics

  8. Variations in EBMT • Database of Sentence Aligned corpus • Analysis of the SL • Depends on how the database is stored • Full sentences, sentence fragments, tree fragments • Matching metric: idea is to arrive at a semantic closeness • Exact match • N-gram match • Fuzzy match • Similarity-based match • Matching with variables • Regeneration of the TL • Depends on how the database produces the output

  9. Issues in EBMT • Parallel corpora • Granularity of examples • Size of example-base • Does accuracy improve by growing example-base? • Suitability of examples • Diversity and consistency of examples • Contradictory examples • Exceptional examples (a) Watashi wa komputa o kyoyosuru I share the use of a computer (b) Watashi wa kuruma o tsukau I use a car (c) Watashi wa dentaku o shiyosuru I share the use of a calculator I use a calculator

  10. Issues in EBMT • How are examples stored? • Context-based examples • “OK” depends on dialog context; • “wakarimashita (I understand)”; • “iidesu yo (I agree)” • or “ijo desu (lets change the subject)” • Annotated tree structures • Eg. Kanojo wa kami ga nagai (She has long hair) • Trees with linking nodes • Multi-level lattices with typographic, orthographic, lexical, syntactic and other information. • Pos information, predicate-argument, chunks, dependency trees • Generalized Examples • Tokenize Dates, Names, cities, gender, number, tense are replaced by generalized tokens • Precision-Recall tradeoff • A continuum from plain strings to context sensitive rules

  11. Issues in EBMT • String based • Sochira ni okeru  We will send it to you • Sochira wa jimukyoku desu  This is the office • Generalized String • X o onegai shimasu  may I speak to the X • X o onegai shimasu  please give me the X • Template Format • N1 N2 N3  N2’ N3’ for N1’ • (N1 = sanka “participation”, N2 = moshikomi “application” N3=yoshi “form”) • Distance in a thesaurus is used to select the method.

  12. Issues in EBMT • Matching: • Metric used to measure the similarity of the SL input to the SLs in the example database. • Exact Character-based matching • Edit-distance based matching • Word-based matching • Thesaurus similarity/Wordnet based similarity • A man eats vegetables  Hito wa yasai o taberu • Acid eats metal  san wa kinzoku o okasu • He eats potatoes  kare wa jagaimo o taberu • Sulphuric acid eats iron  Ryusan wa tetsu o okasu • Thesaurus free similarity matching based on distributional clustering • Annotated word-based matching • POS based matching • Relaxation techniques • Exact match  with dels and insertions  word-order differences morphological variants  POS differences

  13. Matching in EBMT (contd) • Structure-based Matching • Tree-based edit distance • Case-frame based matching • Partial matching • Not entire input need match with the example database • Chunks, substrings, fragments can match • Assembling the TL output is more challenging.

  14. Adaptability and Recombination in EBMT • Problem: • a. Identify which portion of the associated translation corresponds to the matched portion of the source text (Adaptability) • b. Recombining the portions in an appropriate manner. • Alignment: can be done using statistical techniques or using bilingual dictionaries. • Boundary friction problem: For English-Japanese, translations of noun phrases can be reused independent of them being subjects or objects. • The handsome boy entered the room • The handsome boy ate his breakfast • I saw the handsome boy • Not in German: • Der schone Junge aB seine Fruhstuck • Ich sah den schonen Jungen

  15. Adaptability • Example-retrieval can be scored on two counts: • the closeness of the match between the input text and the example, and • the adaptability of the example, on the basis of the relationship between the representations of the example and its translation. • Use the Offset Command to increase the spacing between the shapes. • a. Use the Offset Command to specify the spacing between the shapes. • b. Mit der Option Abstand legen Sie den Abstand zwischen den Formen fest. • a. Use the Save Option to save your changes to disk. • b. Mit der Option Speichern können Sie ihre Anderungen auf Diskette speichern.

  16. Recombination options are ranked using n-gram model a. Ich sah den schönen Jungen. b. * Ich sah der schöne Junge.

  17. Flavors of EBMT • EBMT used as a component in an MT system which also has more traditional elements: • EBMT may be used • in parallel with these other “engines”, • or just for certain classes of problems • when some other component cannot deliver a result. • EBMT may be better suited to some kinds of applications than others. • Dividing line between EBMT and so-called “traditional” rule-based approaches may not be obvious.

  18. When to apply EBMT • When one of the following conditions holds true for a linguistic phenomenon, [rule-based] MT is less suitable than EBMT. • (a) Translation rule formation is difficult. • (b) The general rule cannot accurately describe [the] phenomen[on] because it represents a special case. • (c) Translation cannot be made in a compositional way from target words.

  19. Learning translation patterns • Kare wa kuruma o kuji de ateru. • HE topic CAR obj LOTTERY inst STRIKES • Lit. ‘He strikes a car with the lottery.’ • He wins a car as a prize in the lottery. • Learn pattern (c) from to correct (a) to be like (b)

  20. Generation of Translation Templates • “Two phase” EBMT methodology: “learning” of templates (i.e. transfer rules) from a corpus. • Parse the translation pairs; align the syntactic units with the help of a bilingual dictionary. • Generalized by replacing the coupled units with variables marked for syntactic category. • a. X[NP] no nagasa wa saidai 512 baito de aru.  The maximum length of X[NP] is 512 bytes. • b. X[NP] no nagasa wa saidai Y[N] baito de aru.  The maximum length of X[NP] is Y[N] bytes. • Any coupled unit pair can be replaced by variables. Refine templates which give rise to a conflict • a. play baseball  yakyu o suru • b. play tennis  tenisu o suru • c. play X[NP]!X[NP] o suru • a. play the piano  piano o hiku • b. play the violin  baiorin o hiku • c. play X[NP]!X[NP] o hiku • “refined” by the addition of “semantic categories” • a. play X[NP/sport]  X[NP] o suru • b. play X[NP/instrument]  X[NP] o hiku • Also, automatic generalization techniques from paired strings

  21. Statistical Machine Translation • Can all the steps of EBMT technique be induced from a parallel corpus? • What are the parameters of such a model? • What are the components of SMT? Slides adapted from Dorr and Monz, Knight, Schafer and Smith

  22. Word-Level Alignments • Given a parallel sentence pair we can link (align) words or phrases that are translations of each other: • Where do we get the sentence pairs from?

  23. Parallel Resources • Newswire: DE-News (German-English), Hong-Kong News, Xinhua News (Chinese-English), • Government: Canadian-Hansards (French-English), Europarl (Danish, Dutch, English, Finnish, French, German, Greek, Italian, Portugese, Spanish, Swedish), UN Treaties (Russian, English, Arabic, . . . ) • Manuals: PHP, KDE, OpenOffice (all from OPUS, many languages) • Web pages: STRAND project (Philip Resnik)

  24. Sentence Alignment • If document De is translation of document Df how do we find the translation for each sentence? • The n-th sentence in De is not necessarily the translation of the n-th sentence in document Df • In addition to 1:1 alignments, there are also 1:0, 0:1, 1:n, and n:1 alignments • Approximately 90% of the sentence alignments are 1:1

  25. Sentence Alignment (c’ntd) • There are several sentence alignment algorithms: • Align (Gale & Church): Aligns sentences based on their character length (shorter sentences tend to have shorter translations then longer sentences). Works astonishingly well • Char-align: (Church): Aligns based on shared character sequences. Works fine for similar languages or technical domains • K-Vec (Fung & Church): Induces a translation lexicon from the parallel texts based on the distribution of foreign-English word pairs.

  26. Computing Translation Probabilities • Given a parallel corpus we can estimate P(e | f) The maximum likelihood estimation of P(e | f) is: freq(e,f)/freq(f) • Way too specific to get any reasonable frequencies! Vast majority of unseen data will have zero counts! • P(e | f ) could be re-defined as: • Problem: The English words maximizing • P(e | f ) might not result in a readable sentence

  27. Decoding • The decoder combines the evidence from P(e) and P(f | e) to find the sequence e that is the best translation: • The choice of word e’ as translation of f’ depends on the translation probability P(f’ | e’) and on the context, i.e. other English words preceding e’

  28. Noisy Channel Model for Translation

  29. Translation Modeling • Determines the probability that the foreign word f is a translation of the English word e • How to compute P(f | e) from a parallel corpus? • Statistical approaches rely on the co-occurrence of e and f in the parallel data: If e and f tend to co-occur in parallel sentence pairs, they are likely to be translations of one another

  30. Finding Translations in a Parallel Corpus • Into which foreign words f, . . . , f’ does e translate? • Commonly, four factors are used: • How often do e and f co-occur? (translation) • How likely is a word occurring at position i to translate into a word occurring at position j? (distortion) For example: English is a verb-second language, whereas German is a verb-final language • How likely is e to translate into more than one word? (fertility) For example: defeated can translate into eine Niederlage erleiden • How likely is a foreign word to be spuriously generated? (null translation)

  31. Translation Model? Generative approach: Mary did not slap the green witch Source-language morphological analysis Source parse tree Semantic representation Generate target structure Maria no dió una botefada a la bruja verde

  32. Translation Model? Generative story: Mary did not slap the green witch Source-language morphological analysis Source parse tree Semantic representation Generate target structure What are all the possible moves and their associated probability tables? Maria no dió una botefada a la bruja verde

  33. The Classic Translation ModelWord Substitution/Permutation [IBM Model 3, Brown et al., 1993] Generative approach: Mary did not slap the green witch n(3|slap) Mary not slap slap slap the green witch P-Null Mary not slap slap slap NULL the green witch t(la|the) Maria no dió una botefada a la verde bruja d(j|i) Maria no dió una botefada a la bruja verde Probabilities can be learned from raw bilingual text.

  34. Statistical Machine Translation … la maison … la maison bleue … la fleur … … the house … the blue house … the flower … All word alignments equally likely All P(french-word | english-word) equally likely

  35. Statistical Machine Translation … la maison … la maison bleue … la fleur … … the house … the blue house … the flower … “la” and “the” observed to co-occur frequently, so P(la | the) is increased.

  36. Statistical Machine Translation … la maison … la maison bleue … la fleur … … the house … the blue house … the flower … “house” co-occurs with both “la” and “maison”, but P(maison | house) can be raised without limit, to 1.0, while P(la | house) is limited because of “the” (pigeonhole principle)

  37. Statistical Machine Translation … la maison … la maison bleue … la fleur … … the house … the blue house … the flower … settling down after another iteration

  38. Statistical Machine Translation … la maison … la maison bleue … la fleur … … the house … the blue house … the flower … • Inherent hidden structure revealed by EM training! • For details, see: • “A Statistical MT Tutorial Workbook” (Knight, 1999). • “The Mathematics of Statistical Machine Translation” (Brown et al, 1993) • Software: GIZA++

  39. Statistical Machine Translation … la maison … la maison bleue … la fleur … … the house … the blue house … the flower … P(juste | fair) = 0.411 P(juste | correct) = 0.027 P(juste | right) = 0.020 … Possible English translations, to be rescored by language model new French sentence

  40. IBM Models 1–5 • Model 1: Bag of words • Unique local maxima • Efficient EM algorithm (Model 1–2) • Model 2: General alignment: • Model 3: fertility: n(k | e) • No full EM, count only neighbors (Model 3–5) • Deficient (Model 3–4) • Model 4: Relative distortion, word classes • Model 5: Extra variables to avoid deficiency

  41. IBM Model 1 • Given an English sentence e1 . . . el and a foreign sentence f1 . . . fm • We want to find the ’best’ alignment a, where a is a set pairs of the form {(i , j), . . . , (i’, j’)}, • 0<= i , i’ <= l and 1<= j , j’<= m • Note that if (i , j), (i’, j) are in a, then i equals i’, i.e. no many-to-one alignments are allowed • Note we add a spurious NULL word to the English sentence at position 0 • In total there are (l + 1)m different alignments A • Allowing for many-to-many alignments results in (2l)m possible alignments A

  42. IBM Model 1 • Simplest of the IBM models • Does not consider word order (bag-of-words approach) • Does not model one-to-many alignments • Computationally inexpensive • Useful for parameter estimations that are passed on to more elaborate models

  43. IBM Model 1 • Translation probability in terms of alignments: • where: • and:

  44. IBM Model 1 • We want to find the most likely alignment: • Since P(a | e) is the same for all a: • Problem: We still have to enumerate all alignments

  45. IBM Model 1 • Since P(fj | ei) is independent from P(fj’ | ei’) we can find the maximum alignment by looking at the individual translation probabilities only • Let , then for each aj: • The best alignment can computed in a quadratic number of steps: (l+1 x m)

  46. Computing Model 1 Parameters • How to compute translation probabilities for model 1 from a parallel corpus? • Step 1: Determine candidates. For each English word e collect all foreign words f that co-occur at least once with e • Step 2: Initialize P(f | e) uniformly, i.e. P(f | e) = 1/(no of co-occurring foreign words)

  47. Computing Model 1 Parameters • Step 3: Iteratively refine translation probablities: • 1 for n iterations • 2 set tc to zero • 3 for each sentence pair (e,f) of lengths (l,m) • 4 for j=1 to m • 5 total=0; • 6 for i=1 to l • 7 total += P(fj | ei); • 8 for i=1 to l • 9 tc(fj | ei) += P(fj | ei)/total; • 10 for each word e • 11 total=0; • 12 for each word f s.t. tc(f | e) is defined • 13 total += tc(f | e); • 14 for each word f s.t. tc(f | e) is defined • 15 P(f | e) = tc(f | e)/total;

  48. IBM Model 1 Example • Parallel ‘corpus’: the dog :: le chien the cat :: le chat • Step 1+2 (collect candidates and initialize uniformly): P(le | the) = P(chien | the) = P(chat | the) = 1/3 P(le | dog) = P(chien | dog) = P(chat | dog) = 1/3 P(le | cat) = P(chien | cat) = P(chat | cat) = 1/3 P(le | NULL) = P(chien | NULL) = P(chat | NULL) = 1/3

  49. IBM Model 1 Example • Step 3: Iterate • NULL the dog :: le chien • j=1 total = P(le | NULL)+P(le | the)+P(le | dog)= 1 tc(le | NULL) += P(le | NULL)/1 = 0 += .333/1 = 0.333 tc(le | the) += P(le | the)/1 = 0 += .333/1 = 0.333 tc(le | dog) += P(le | dog)/1 = 0 += .333/1 = 0.333 • j=2 total = P(chien | NULL)+P(chien | the)+P(chien | dog)=1 tc(chien | NULL) += P(chien | NULL)/1 = 0 += .333/1 = 0.333 tc(chien | the) += P(chien | the)/1 = 0 += .333/1 = 0.333 tc(chien | dog) += P(chien | dog)/1 = 0 += .333/1 = 0.333

  50. IBM Model 1 Example • NULL the cat :: le chat • j=1 total = P(le | NULL)+P(le | the)+P(le | cat)=1 tc(le | NULL) += P(le | NULL)/1 = 0.333 += .333/1 = 0.666 tc(le | the) += P(le | the)/1 = 0.333 += .333/1 = 0.666 tc(le | cat) += P(le | cat)/1 = 0 +=.333/1 = 0.333 • j=2 total = P(chien | NULL)+P(chien | the)+P(chien | dog)=1 tc(chat | NULL) += P(chat | NULL)/1 = 0 += .333/1 = 0.333 tc(chat | the) += P(chat | the)/1 = 0 += .333/1 = 0.333 tc(chat | cat) += P(chat | dog)/1 = 0 += .333/1 = 0.333