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

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Presentation on theme: "Fall 2004 Lecture Notes #7 EECS 595 / LING 541 / SI 661 Natural Language Processing."— Presentation transcript:

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

2 Machine Translation

3 Example (from the Hansards corpus) English 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. If the minister would like to propose that, I for one would be prepared to support him. French 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. Si le ministre voulait proposer cela, je serais pour ma part disposé à l'appuyer.

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

5 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)

6 Why is Machine Translation Hard? Analysis Transfer/interlingua Generation INPUT OUTPUT 2 OUTPUT 1 OUTPUT 3

7 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

8 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)

9 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)

10 Homography and Polysemy Homographs: (“light”, “club”, “bank”) Polysemous words: (“channel”, “crane”) for different categories - syntax for same category - semantics

11 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

12 Use of Linguistic Knowledge Subcategorization frames Semantic features (is an object “readable”?)

13 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

14 Other Strategies Pick most natural interpretation Ask the author Make a guess Hope for a free ride Direct transfer

15 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)

16 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

17 The Noisy Channel Model Source-channel model of communication Parametric probabilistic models of language and translation Training such models

18 Statistics Given f, guess e e f e’ E  FF  E encoderdecoder e’ = argmax P(e|f) = argmax P(f|e) P(e) e e translation modellanguage model

19 Parametric probabilistic models Language model (LM) Deleted interpolation Translation model (TM) P(e) = P(e 1, e 2, …, e L ) = P(e 1 ) P(e 2 |e 1 ) … P(e L |e 1 … e L-1 ) P(e L |e 1 … e K-1 )  P(e L |e L-2, e L-1 ) Alignment: P(f,a|e)

20 IBM’s EM trained models Word translation Local alignment Fertilities Class-based alignment Non-deficient algorithm (avoid overlaps, overflow)

21 Evaluation Human judgements: adequacy, grammaticality Automatic methods –BLEU –ROUGE

22 Readings for next time J&M Chapters 18, 21

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