Machine Translation Dr. Nizar Habash Research Scientist Center for Computational Learning Systems...

Machine Translation

Dr. Nizar HabashResearch Scientist

Center for Computational Learning SystemsColumbia University

COMS E6998: Topics in Computer Science

Spring 2013

Session #1

• Introductions• Syllabus Explanation• Lecture

– Why Machine Translation– Multilingual Challenges for MT– MT Approaches– MT Evaluation

Why (Machine) Translation?

Languages in the world• 6,800 living languages• 600 with written tradition • 100 languages are spoken by 95% of world population

Translation Market• $26 Billion Global Market

(2010)• Doubling every five years

(Donald Barabé, invited talk, MT Summit 2003)

MultilingualismTower of Babel

• Genesis 11:1-91 And the whole earth was of one

language, and of one speech....9 Therefore is the name of it called

Babel; because the Lord did there confound the language of all the earth: and from thence did the Lord scatter them abroad upon the face of all the earth.

• Foremost symbol of multilingualism as a problem

MultilingualismLanguage Families

MultilingualismRosetta Stone

• Ancient Egyptian stele (196 BCE )• Key to modern understanding of

Egyptian hieroglyphs• Trilingual document:

– ancient Egyptian hieroglyphs– Egyptian demotic script– ancient Greek

• Common symbol of parallel corpora and translation solutions

Modern Rosetta Stones?

Multilingual Challenges

• nai you duo shi means buttered toast• naiyou means butter• duoshi means toast• duo means many • shi can mean private (as in the army rank)

Shatt Al-Arab Fresh Fish

Why (Machine) Translation?

Languages in the world• 6,800 living languages• 600 with written tradition • 100 languages are spoken by 95% of world population

Translation Market• $26 Billion Global Market

(2010)• Doubling every five years

(Donald Barabé, invited talk, MT Summit 2003)

Machine TranslationScience Fiction

• Star Trek Universal Translator an "extremely sophisticated computer

program" which functions by "analyzing the patterns" of an unknown foreign language, starting from a speech sample of two or more speakers in conversation. The more extensive the conversational sample, the more accurate and reliable is the "translation matrix"….


• Futurama Universal TranslatorDr. Farnsworth: “This is my

Universal Translator, although it only translate into an incomprehensible dead language”

Cubert: “Hello!” Machine: “Bonjour!”Dr. Farnsworth: "Imcomprehensible gibberish”


• The Babel FishThe Hitch Hiker's Guide to the Galaxy" (Douglas Adams)

"is small, yellow and leech-like, ... if you stick a Babel fish in your ear you can instantly understand anything said to you in any form of language…"

Machine Translation Reality

http://www.medialocate.com/

Machine Translation Reality

• Currently, Google offers translations between the following languages over 3,000 pairs

Afrikaans

Albanian

Arabic

Armenian

Azerbaijani

Basque

Belarusian

Bulgarian

Catalan

Chinese

Croatian

Czech

Danish

Dutch

English

Estonian

Filipino

Finnish

French

Galician

Georgian

German

Greek

Haitian Creole

Hebrew

Hindi

Hungarian

Icelandic

Indonesian

Irish

Italian

Japanese

Korean

Latvian

Lithuanian

Macedonian

Malay

Maltese

Norwegian

Polish

Portuguese

Romanian

Russian

Serbian

Slovak

Slovenian

Spanish

Swahili

Swedish

Thai

Turkish

Ukrainian

Urdu

Vietnamese

Welsh

Yiddish

“BBC found similar support”!!!

Why Machine Translation?• Full Translation

– Domain specific, e.g., Weather reports

• Machine-aided Translation– Requires post-editing

• Cross-lingual NLP applications– Cross-language IR– Cross-language Summarization

• Testing grounds – Extrinsic evaluation of NLP tools, e.g., parsers, pos

taggers, tokenizers, etc.

Road Map

• Multilingual Challenges for MT• MT Approaches• MT Evaluation

Multilingual Challenges

• Orthographic Variations– Ambiguous spelling

• اشعارا االوالد ك�ت�ب� كتب اشع�ارا األو�الد�

– Ambiguous word boundaries•

• Lexical Ambiguity– Bank بنك (financial) vs. ضفة (river)– Eat essen (human) vs. fressen (animal)

Multilingual Challenges Morphological Variations

• Affixational (prefix/suffix) vs. Templatic (Root+Pattern)

write written كتب

بوكتم

kill killed قتل لوقتم

do done فعل لوفعم

conj

noun

pluralarticle

• Tokenization (aka segmentation+normalization)

And the cars and the cars

اتسيارالو w Al SyArAt

Et les voitures et le voitures

Morphology

• Arabic: very rich morphology: number, gender, case, person, aspect, voice, several clitics, etc.– Arabic tokenization

• English: simple morphology • Chinese: no morphology – quantifiers & verbal aspects

الصف كتاباالمجتهد الطالبيقرأ في الصين عنread the-student the-diligent a-book about china in the-classroom

the diligent student is reading a book about china in the classroom

这位勤奋的学生在教室读一本关于中国的书this quant diligent de student in classroom read one quant about china de book

Syntax

Arabic English ChineseSubj-Verb V Subj Subj V Subj V Subj … V

Verb-PP V…PP V…PP V PP PP V

Adjectives N Adj Adj N Adj de N

Possessives N Poss N of Poss Poss ’s N Poss de N

Relatives N Rel N Rel Rel de N

المجتهد يقرأ الصين الطالب عن الصف كتابا فيread the-student the-diligent a-book about china in the-classroom



Syntaxالصين كتابا المجتهد الطالبيقرأ الصف عن في

read the-student the-diligent a-book about china in the-classroom



Arabic English ChineseSubj-Verb V Subj Subj V Subj V Subj … V

Verb-PP V…PP V…PP V PP PP V

Adjectives N Adj Adj N Adj de N

Possessives N Poss N of Poss Poss ’s N Poss de N

Relatives N Rel N Rel Rel de N

هنا لستI-am-not here

am

I here

I am not here

not

تلس

هنا

Translation Divergencesconflation

Je ne suis pas iciI not am not here

suis

Je icine pas

*نا ا بردان

*קר ל

بردانانا I cold

be

I cold

I am cold ליקרcold for-me

אני

Translation Divergencescategorial, thematic and structural

tener

Yo frio

tengo frioI-have cold

swim

I quicklyacross

river

I swam across the river quickly

Translation Divergenceshead swap and categorial

اسرع

انا عبورسباحة

نهر

سباحة النهر عبور اسرعتI-sped crossing the-river swimming

swim

I quicklyacross

river

I swam across the river quickly

Translation Divergences head swap and categorial

חצה

אני אתב

נהר

ב

שחיה מהירות

חציתי את הנהר בשחיה במהירותI-crossed obj river in-swim speedily

Translation Divergences head swap and categorial

חצה

אני אתב

נהר

ב

שחיה מהירות

اسرع

انا عبورسباحة

نهر

swim

I quicklyacross

river

noun

prep

verb

noun

adverb

verb

nounverb

noun

Translation DivergencesOrthography+Morphology+Syntax

妈妈的车 mama de che

car

mom

possessed-by

mom’s car

ماما ةسيارsayyArat mama

la voiture de maman

Road Map

• Multilingual Challenges for MT• MT Approaches• MT Evaluation

Knowledge Acquisition Strategy

Knowledge Representation Strategy

All manual

Deep/ Complex

Shallow/ Simple

Fully automated

Learn from un-annotated data

Phrase tables

Word-based only

Learn from annotated data

Example-based MT

Original statistical MT

Typical transfer system

Classic interlingual system

Original direct approach

Syntactic Constituent Structure

Interlingua

New Research Goes Here!

Semantic analysis

Hand-built by non-experts

Hand-built by experts

Electronic dictionaries

MT Strategies (1954-2004)

Slide courtesy ofLaurie Gerber

MT ApproachesMT Pyramid

Source word

Source syntax

Source meaning Target meaning

Target syntax

Target word

Analysis Generation

Gisting

MT ApproachesGisting Example

Sobre la base de dichas experiencias se estableció en 1988 una metodología.

Envelope her basis out speak experiences them settle at 1988 one methodology.

On the basis of these experiences, a methodology was arrived at in 1988.


Source word

Source syntax


Target syntax

Target word

Analysis Generation

Gisting

Transfer

MT ApproachesTransfer Example

• Transfer Lexicon – Map SL structure to TL structure

poner

X mantequilla en

Y

:obj:mod:subj

:obj

butter

X Y

:subj :obj

X puso mantequilla en Y X buttered Y


Source word

Source syntax


Target syntax

Target word

Analysis Generation

Gisting

Transfer

Interlingua

MT ApproachesInterlingua Example: Lexical Conceptual Structure

(Dorr, 1993)


Source word

Source syntax


Target syntax

Target word

Analysis Generation

Interlingua

Gisting

Transfer


Source word

Source syntax


Target syntax

Target word

Analysis Generation

Interlingual Lexicons

Dictionaries/Parallel Corpora

Transfer Lexicons

MT ApproachesStatistical vs. Rule-based

Source word

Source syntax


Target syntax

Target word

Analysis Generation

To be continued …

Machine Translation Dr. Nizar Habash Research Scientist Center for Computational Learning Systems...

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