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Modeling morphology in translation

Edinburgh MT lecture 14: Modeling morphology in translation

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Page 1: Edinburgh MT lecture 14: Modeling morphology in translation

Modeling morphology in translation

Page 2: Edinburgh MT lecture 14: Modeling morphology in translation

Inflectional morphologyWord form changes to reflect case, person,

number, gender, animacy, tense, mood, definiteness, social status, negation, etc.

Page 3: Edinburgh MT lecture 14: Modeling morphology in translation

Inflectional morphologyWord form changes to reflect case, person,

number, gender, animacy, tense, mood, definiteness, social status, negation, etc.

Politeness to addressee

Referent honorific

Both

Page 4: Edinburgh MT lecture 14: Modeling morphology in translation

Inflectional morphologyInflection is not just concatenation

Reduplication

Infixation

Vowel harmony

Page 5: Edinburgh MT lecture 14: Modeling morphology in translation

Inflectional morphologyInflection systematically increases the number of word forms that a system must understand and/ or generate. So it affects data sparsity (if we only model word forms).

Page 6: Edinburgh MT lecture 14: Modeling morphology in translation

Inflectional morphologyIt also affects meaning

cf. “Kim loves Pat” vs. “Pat loves Kim” (syntax also affects meaning)

Page 7: Edinburgh MT lecture 14: Modeling morphology in translation

Agglutinationostoskeskuksessa

ostos#keskus+N+Sg+Loc:inshopping#center+N+Sg+Loc:in

‘in the shopping center’

ᖃᖓᑕᓲᒃᑯᕕᒻᒨᕆᐊᖃᓛᖅᑐᖓ qangatasuukkuvimmuuriaqalaaqtunga

“I'll have to go to the airport”

Page 8: Edinburgh MT lecture 14: Modeling morphology in translation

A basic oracle experiment (Goldwater & mcClosky 2005)

Translating from rich inflections

Page 9: Edinburgh MT lecture 14: Modeling morphology in translation

Translating from rich inflectionsA basic oracle experiment (Goldwater & mcClosky 2005)

Page 10: Edinburgh MT lecture 14: Modeling morphology in translation

Translating from rich inflectionsA basic oracle experiment (Goldwater & mcClosky 2005)

Page 11: Edinburgh MT lecture 14: Modeling morphology in translation

Translating from rich inflectionsA basic oracle experiment (Goldwater & mcClosky 2005)

Page 12: Edinburgh MT lecture 14: Modeling morphology in translation

Translating from rich inflectionsA basic oracle experiment (Goldwater & McClosky 2005)

Page 13: Edinburgh MT lecture 14: Modeling morphology in translation

Translating from rich inflectionsA basic oracle experiment (Goldwater & mcClosky 2005)

Encouraging, but in most data, we don’t have the morphological analysis (and it is ambiguous)

Page 14: Edinburgh MT lecture 14: Modeling morphology in translation

Translating from rich inflections

Rather than decide on a particular representation of words, allow translation system to choose.

Page 15: Edinburgh MT lecture 14: Modeling morphology in translation

Translating from rich inflections

Rather than decide on a particular representation of words, allow translation system to choose.

Page 16: Edinburgh MT lecture 14: Modeling morphology in translation

Translating from compounding

Page 17: Edinburgh MT lecture 14: Modeling morphology in translation

Translating from compoundingByte pair encoding:

iteratively replace most common bigram with new

symbol (stop using heuristic) aaabdaaabac ZabdZabac

ZYdZYac XdXac

When applied to words:

Page 18: Edinburgh MT lecture 14: Modeling morphology in translation

Morphological analysis

прочий +Adj +Sg +Neut +Instrпрочий +Adj +Sg +Masc +Instrпрочий +Adj +Pl +Datпрочить +Verb +Pl +1Pпрочее +Pro +Sg +Ins

прочим

… is ambiguous

word

possible analyses

But… generation is generally unambiguous

Page 19: Edinburgh MT lecture 14: Modeling morphology in translation

Translating to rich inflections

Page 20: Edinburgh MT lecture 14: Modeling morphology in translation

Translating to rich inflections

genitive case: marks noun modifying another noun

Page 21: Edinburgh MT lecture 14: Modeling morphology in translation

Translating to rich inflections

verb and its argument must agree on these features

Page 22: Edinburgh MT lecture 14: Modeling morphology in translation

Translating to rich inflections

p(y|x) =nY

t=1

p(yt|yt�1...yt�k, xt)

Page 23: Edinburgh MT lecture 14: Modeling morphology in translation

Translating to rich inflections

p(y|x) =nY

t=1

p(yt|yt�1...yt�k, xt)

Page 24: Edinburgh MT lecture 14: Modeling morphology in translation

Translating to rich inflections

p(y|x) =nY

t=1

p(yt|yt�1...yt�k, xt)

Page 25: Edinburgh MT lecture 14: Modeling morphology in translation

Translating to rich inflections

Page 26: Edinburgh MT lecture 14: Modeling morphology in translation

Translating to rich inflections

Page 27: Edinburgh MT lecture 14: Modeling morphology in translation

Handling unknown input words

Word representations composed from characters can be applied to any word, even when unseen.

Page 28: Edinburgh MT lecture 14: Modeling morphology in translation

Generating novel output words

Character-based language models can generate any word

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Character-based neural MT

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Character-based neural MT

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English & German

Page 32: Edinburgh MT lecture 14: Modeling morphology in translation

English & German

Page 33: Edinburgh MT lecture 14: Modeling morphology in translation

English & German

Page 34: Edinburgh MT lecture 14: Modeling morphology in translation

English & German

Page 35: Edinburgh MT lecture 14: Modeling morphology in translation

English & Arabic

Page 36: Edinburgh MT lecture 14: Modeling morphology in translation

English & Arabic

Page 37: Edinburgh MT lecture 14: Modeling morphology in translation

English & Arabic

Page 38: Edinburgh MT lecture 14: Modeling morphology in translation

English & Arabic

Page 39: Edinburgh MT lecture 14: Modeling morphology in translation

English & Arabic

Page 40: Edinburgh MT lecture 14: Modeling morphology in translation

English & Turkish

Page 41: Edinburgh MT lecture 14: Modeling morphology in translation

English & Turkish

Page 42: Edinburgh MT lecture 14: Modeling morphology in translation

Word Order: Syntax

The brown dog on the mat saw the striped cat through the window. The brown cat saw the striped dog through the window on the mat.

i.e. the difference between a sentence and a bag of words.

Q: Does IBM Model 1 give these translations different probabilities (given a fixed conditioning sentence)?

Page 43: Edinburgh MT lecture 14: Modeling morphology in translation

Garcia and associates .

Garcia y asociados .Carlos Garcia has three associates .

Carlos Garcia tiene tres asociados .his associates are not strong .

sus asociados no son fuertes .Garcia has a company also .

Garcia tambien tiene una empresa .its clients are angry .

sus clientes estan enfadados .the associates are also angry .

los asociados tambien estan enfadados .

la empresa tiene enemigos fuertes en Europa .

the company has strong enemies in Europe .the clients and the associates are enemies .

los clientes y los asociados son enemigos .the company has three groups .

la empresa tiene tres grupos .its groups are in Europe .

sus grupos estan en Europa .the modern groups sell strong pharmaceuticals .

los grupos modernos venden medicinas fuertes .the groups do not sell zanzanine .

los grupos no venden zanzanina .the small groups are not modern .

los grupos pequenos no son modernos .

Page 44: Edinburgh MT lecture 14: Modeling morphology in translation

Garcia and associates .

Garcia y asociados .Carlos Garcia has three associates .

Carlos Garcia tiene tres asociados .his associates are not strong .

sus asociados no son fuertes .Garcia has a company also .

Garcia tambien tiene una empresa .its clients are angry .

sus clientes estan enfadados .the associates are also angry .

los asociados tambien estan enfadados .

la empresa tiene enemigos fuertes en Europa .

the company has strong enemies in Europe .the clients and the associates are enemies .

los clientes y los asociados son enemigos .the company has three groups .

la empresa tiene tres grupos .its groups are in Europe .

sus grupos estan en Europa .the modern groups sell strong pharmaceuticals .

los grupos modernos venden medicinas fuertes .the groups do not sell zanzanine .

los grupos no venden zanzanina .the small groups are not modern .

los grupos pequenos no son modernos .

Page 45: Edinburgh MT lecture 14: Modeling morphology in translation

Garcia and associates .

Garcia y asociados .Carlos Garcia has three associates .

Carlos Garcia tiene tres asociados .his associates are not strong .

sus asociados no son fuertes .Garcia has a company also .

Garcia tambien tiene una empresa .its clients are angry .

sus clientes estan enfadados .the associates are also angry .

los asociados tambien estan enfadados .

la empresa tiene enemigos fuertes en Europa .

the company has strong enemies in Europe .the clients and the associates are enemies .

los clientes y los asociados son enemigos .the company has three groups .

la empresa tiene tres grupos .its groups are in Europe .

sus grupos estan en Europa .the modern groups sell strong pharmaceuticals .

los grupos modernos venden medicinas fuertes .the groups do not sell zanzanine .

los grupos no venden zanzanina .the small groups are not modern .

los grupos pequenos no son modernos .

Same pattern:NN JJ → JJ NN

Page 46: Edinburgh MT lecture 14: Modeling morphology in translation

Garcia and associates .

Garcia y asociados .Carlos Garcia has three associates .

Carlos Garcia tiene tres asociados .his associates are not strong .

sus asociados no son fuertes .Garcia has a company also .

Garcia tambien tiene una empresa .its clients are angry .

sus clientes estan enfadados .the associates are also angry .

los asociados tambien estan enfadados .

la empresa tiene enemigos fuertes en Europa .

the company has strong enemies in Europe .the clients and the associates are enemies .

los clientes y los asociados son enemigos .the company has three groups .

la empresa tiene tres grupos .its groups are in Europe .

sus grupos estan en Europa .the modern groups sell strong pharmaceuticals .

los grupos modernos venden medicinas fuertes .the groups do not sell zanzanine .

los grupos no venden zanzanina .the small groups are not modern .

los grupos pequenos no son modernos .

Same pattern:NN JJ → JJ NN

Phrase-based models do not capture this generalization.

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