TOPIC 6: MACHINE TRANSLATION

NATURAL LANGUAGE PROCESSING (NLP)

CS-724

WondwossenMulugeta (PhD) email: wondemule@yahoo.com

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Topics2

Topics Subtopics6: Machine Translation

• Introduction, • Challenges, • Application, • Approaches

Why (Machine) Translation?

Languages in the world• 6,800 living languages

• 600 with written tradition

• 95% of world population

speaks 100 languages

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Definitions

“Machine translation (MT) is the application of computers to the task of translating texts from one natural language to another.” European Association for Machine Translation

“…Machine Translation (MT) as it is generally known --- the attempt to automate all, or part of the process of translating from one human language to another.” Arnold D J. MACHINE TRANSLATION: An Introductory Guide

Currently, Google supports 103 languages which offers

translations between the following languages over

3,000 pairs

Amharic

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

English-Amharic…Google Translate6

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

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Why is translation hard

Two/three steps involved:

“Understand” source text

Convert that into target language

Generate correct target text

Depends on approach

Rule-Based vs Statistical

Understanding source text involves same

problems as for any NLP application

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Understanding the source text

Lexical ambiguity At morphological level Ambiguity of word vs stem+ending (tower, flower)

Very important and complex for Amharic Grammatical category ambiguity (eg close) Homonymy ... Identical words but different meaning Alternate meanings within same grammatical category (eg: Bank)

Syntactic ambiguity (deep) Due to combination of grammatically ambiguous words

Time flies like an arrow, fruit flies like a banana

(shallow) Due to alternative interpretations of structure The man saw the girl with a telescope

the girl having a telescope or

The man used telescope to see the girl

Multilingual Challenges

Orthographic Variations

Ambiguous spelling

كتب االوالد اشعارا َكتََب األْوالدُ اشعَارا

Ambiguous boundaries

ትመጫለሽ where is the boundary to separate the stem from the affix?

Lexical Ambiguity

Bank بنك (financial) vs. ضفة (river)

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MT Challenges: Ambiguity

Syntactic AmbiguityI saw the man with the telescope

S

NP VP

VP PPI

With thetelescope

S

NP VP

NP

PP

I

the manWith thetelescope

NP

saw

V

the man

NPsaw

V

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MT Challenges: Ambiguity

Syntactic AmbiguityI saw the man on the hill with the telescope

Lexical AmbiguityE: book

(the writing material or the action of reserving a place?)

Semantic Ambiguity Homography:

ball(E) = pelota, baile(S)

Polysemy:kill(E), matar, acabar (S)

Semantic granularityesperar(S) = wait, expect, hope (E)be(E) = ser, estar(S)fish(E) = pez, pescado(S)

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Approaches to Machine Translation

Rule-based

Statistical Approaches

Hybrid Systems (Using Statistical approach in an

Rule-based Architecture or … )

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.

MT ApproachesMT Pyramid

Source word

Source syntax

Source meaning Target meaning

Target syntax

Target word

Analysis Generation

Gisting

Transfer

MT Approaches

Transfer 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

MT ApproachesMT Pyramid

Source word

Source syntax

Source meaning Target meaning

Target syntax

Target word

Analysis Generation

Gisting

Transfer

Interlingua

MT ApproachesInterlingua Example: Lexical Conceptual Structure

MT ApproachesMT Pyramid

Source word

Source syntax

Source meaning Target meaning

Target syntax

Target word

Analysis Generation

Interlingual Lexicons

Dictionaries/Parallel Corpora

Transfer Lexicons

Statistical MT

Automatic Word Alignment

GIZA++ A statistical machine translation toolkit used to train word

alignments (we need language pairs).

Uses different algorithms and approaches to bootstrap alignments

Mary

did

not

slap

the

green

witch

Maria no dio una bofetada a la bruja verde

Phrase-Based Statistical MT

• Foreign input segmented in to phrases

– “phrase” is any sequence of words

• Each phrase is probabilistically translated into English

– P(to the conference | zur Konferenz)

– P(into the meeting | zur Konferenz)

• Phrases are probabilistically re-ordered

This is state-of-the-art!

Morgen fliege ich nach Kanada zur Konferenz

Tomorrow I will fly to the conference In Canada

MT Approaches

Practical Considerations

Resources Availability

Parsers and Generators

Input/Output compatibility

Translation Lexicons

Word-based vs. Transfer/Interlingua

Parallel Corpora

Domain of interest

Bigger is better (more data more varieties and accuracy)

Time Requirement

Statistical training,

resource building

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Moses statistical MT

Open source MT system developed with C++ programming language

allows to automatically train translation models for any language pair.

Requirement: a collection of translated texts (parallel corpus).

phrase-based

Example-based MT

Long-established approach to empirical MT

First developed in contrast with rule-based MT

Idea of translation by analogy Translate by

adapting previously seen examples rather than by

linguistic rule

“Existing translations contain more solutions to

more translation problems than any other

available resource.” In computational terms,

belongs in family of Case-based reasoning

approaches

EBMT basic idea

Requires database of translation pairs

match input against example database

(like Translation Memory)

identify corresponding translation fragments

(align)

recombine fragment into target text

He buys a book on international politics

Input

Matches

He buys a notebook.

Kare wa nōto o kau.

I read a book on international politics.

Watashi wa kokusai seiji nitsuite kakareta hon o yomu.

Result

Kare wa o kau.kokusai seiji nitsuite kakareta hon

Example

EBMT vs SMT

SMT essentially uses

statistical data (parameters,

probabilities) derived from

the bitext

Pre-processing the data is

essential

Even if the input is in the

training data, you are not

guaranteed to get the same

translation

EBMT uses the bitext as its

primary data source

Pre-processing the data is

optional

If the input is in the

example set, you are

guaranteed to get the same

translation

SMT EBMT

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MT Evaluation

Translation System evaluation is more of an art than

science… We are concerned about how people

feel about the translation

Wide range of Metrics/Techniques

interface, …,

scalability, …,

faithfulness, ...

space/time complexity, … etc.

Automatic vs. Human-based

Dumb Machines vs. Slow Humans

Automatic Evaluation Example

Bleu Metric

Statistical evaluation method

Uses modified n-gram precision with length penalty

(starts from unigram and goes to a higher window of

words)

Quick, inexpensive and language independent

(string matching….word or phrase level)

Correlates highly with human evaluation

Humans process translation by fragments

Suffers from bias against synonyms and

inflectional variations

Test Sentence

colorless green ideas sleep furiously

Gold Standard References

(Human Translation)

all dull jade ideas sleep irately

drab emerald concepts sleep furiously

colorless immature thoughts nap angrily

We have five words in the test sentences. What we have found in

the Gold Standard is only four of the words in the test sentences.

Thus:

• Unigram precision = 4/5

Automatic Evaluation Example

Bleu Metric

Test Sentence

colorless green ideas sleep furiously

colorless green ideas sleep furiously

colorless green ideas sleep furiously

colorless green ideas sleep furiously

Gold Standard References

(Human Translation)

all dull jade ideas sleep irately

drab emerald concepts sleep furiously

colorless immature thoughts nap angrily

Unigram precision = 4 / 5 = 0.8

Bigram precision = 2 / 4 = 0.5

Bleu Score = (a1 a2 …an)1/n

= (0.8 x 0.5)½ = (0.4)½ = 0.6325 63.25%

Automatic Evaluation Example

Bleu Metric

5 contents of original sentence conveyed (might need minor corrections)

4 contents of original sentence conveyed BUT errors in word order

3 contents of original sentence generally conveyed BUT errors in relationship between phrases, tense, singular/plural, etc.

2 contents of original sentence not adequately conveyed, portions of original sentence incorrectly translated, missing modifiers

1 contents of original sentence not conveyed, missing verbs, subjects, objects, phrases or clauses

Human-based Evaluation Example

Accuracy Criteria

Perform this scoring for each sentences of the test cases and take the average.

5 clear meaning, good grammar, terminology and sentence structure

4 clear meaning BUT bad grammar, bad terminology or bad sentence structure

3 meaning graspable BUT ambiguities due to bad grammar, bad terminology or bad sentence structure

2 meaning unclear BUT inferable

1 meaning absolutely unclear

Human-based Evaluation Example

Fluency Criteria

Perform this scoring for each sentences of the test cases and take the average.

End of Topic 6