Fall 2004 Lecture Notes #7 EECS 595 / LING 541 / SI 661 Natural Language Processing.

Fall 2004

Lecture Notes #7

EECS 595 / LING 541 / SI 661

Natural Language Processing

Machine Translation

Example (from the Hansards corpus)

• English• <s id=960001> I would like the government and the Postmaster General to

agree that we place the union and the Postmaster General under trusteeship so that we can look at his books and records, including those of his management people and all the memos he has received from them, some of which must have shocked him rigid.

• <s id=960002> If the minister would like to propose that, I for one would be prepared to support him.

• French• <s id=960001> Je voudrais que le gouvernement et le ministre des Postes

conviennent de placer le syndicat et le ministre des Postes sous tutelle afin que nous puissions examiner ses livres et ses dossiers, y compris ceux de ses collaborateurs, et tous les mémoires qu'il a reçus d'eux, dont certains l'ont sidéré.

• <s id=960002> Si le ministre voulait proposer cela, je serais pour ma part disposé à l'appuyer.

Example

• These lies are like their father that begets them; gross as a mountain, open, palpable(Henry IV, Part 1, act 2, scene 2)

Language similarities and differences

• Word order (SVO: English, Mandarin, VSO: Irish, Classical Arabic, SOV: Hindi, Japanese)

• Prepositions (Jap.) (to Mariko, Mariko-ni)• Lexical distinctions (Sp.):

– the bottle floated out

– la botella salió flotando

• Brother (Jap.) = otooto (younger), oniisan (older)• They (Fr.) = elles (feminine), ils (masculine)

Acrobat Document

Why is Machine Translation Hard?

• Analysis• Transfer/interlingua• Generation

INPUT OUTPUT2OUTPUT2OUTPUT2

OUTPUT1

OUTPUT3

Basic Strategies of MT

• Direct Approach– 50’s,60’s – naïve

• Indirect: Interlingua– No looking back– Language-neutral– No influence on the target language

• Indirect: Transfer– Preferred

F E

I

Levels of Linguistic Processing

• Phonology

• Orthography

• Morphology (inflectional, derivational)

• Syntax (e.g., agreement)

• Semantics (e.g., concrete vs. abstract terms)

• Discourse (e.g., use of pronouns)

• Pragmatics (world knowledge)

Category Ambiguity

• Morphological ambiguity (“Wachtraum”)

• Part-of-speech (category) ambiguity (e.g. “round”)

• Some help comes from morphology (“rounding”)

• Using syntax, some ambiguities disappear (context dictates category)

Homography and Polysemy

• Homographs: (“light”, “club”, “bank”)

• Polysemous words: (“channel”, “crane”)

• for different categories - syntax

• for same category - semantics

Structural Ambiguity

• Humans can have multiple interpretations (parses) for the same sentence

• Example: prepositional phrase attachment

• Use context to disambiguate

• For machine translation, context can be hard to define

Use of Linguistic Knowledge

• Subcategorization frames

• Semantic features (is an object “readable”?)

Contextual Knowledge

• In practice, very few sentences are truly ambiguous

• Context makes sense for humans (“telescope” example), not for machines

• no clear definition of context

Other Strategies

• Pick most natural interpretation

• Ask the author

• Make a guess

• Hope for a free ride

• Direct transfer

Acrobat DocumentAcrobat DocumentAcrobat Document

Anaphora Resolution

• Use of pronouns (“it”, “him”, “himself”, “her”)

• Definite anaphora (“the young man”)

• Antecedents

• Same problems as for ambiguity resolution

• Similar solutions (e.g., subcategorization)

When does MT work?

• Machine-Aided Translation (MAT)

• Restricted Domains (e.g., technical manuals)

• Restricted Languages (sublanguages)

• To give the reader an idea of what the text is about

The Noisy Channel Model

• Source-channel model of communication

• Parametric probabilistic models of language and translation

• Training such models

Statistics

• Given f, guess e

ef

e’E F F E

encoder decoder

e’ = argmax P(e|f) = argmax P(f|e) P(e)e e

translation model language model

Parametric probabilistic models

• Language model (LM)

• Deleted interpolation

• Translation model (TM)

P(e) = P(e1, e2, …, eL) = P(e1) P(e2|e1) … P(eL|e1 … eL-1)

P(eL|e1 … eK-1) P(eL|eL-2, eL-1)

Alignment: P(f,a|e)

IBM’s EM trained models

• Word translation

• Local alignment

• Fertilities

• Class-based alignment

• Non-deficient algorithm (avoid overlaps, overflow)

Evaluation

• Human judgements: adequacy, grammaticality

• Automatic methods– BLEU– ROUGE

Readings for next time

• J&M Chapters 18, 21