Motivation on Ontology Translation · Motivation on Ontology Translation Business Information query in English fixed assets@en . ... Staaten | an diesem Punkt | geht so | weit wie

Motivation on Ontology Translation

Business Information query in English fixed assets@en

Motivation on Ontology Translation

Business Information query in English fixed assets@en ; Vermögensgegenstände@de ; activo fijo@es ...

Outline

• Statistical Machine Translation (SMT)

– Insights into SMT

• Translation Models (TM) / Language model (LM)

• Word alignment -> Phrase-Based SMT

• Ontology Translation and SMT

– Domain adaptation, Term Identification

– UNLP SMT Demos

• TeTra, OTTO, Iris

Insights into SMT

• Statistical Machine Translation

– Translation Model

• lexical correspondence between languages

– Language Model

• takes care of fluency (and lexical choice) in the target language

fixed asset | anlagevermögens | 0.003 0.003 0.029 0.102

-4.868038 anlagevermögens -0.1317768

Training Data (Parallel Corpora) for SMT

Source document Target document

• Sentence aligned parallel data

Training Data for SMT

sentence one with some words sentence two with more words ...

satz eins mit einigen wörtern satz zwei mit weiteren wörtern ...

• Parallel data

Training Data for SMT

sentence one with some words sentence two with more words ...

satz eins mit einigen wörtern satz zwei mit weiteren wörtern ...

Word Alignment

Word Alignment Scenarios





Word Alignment with IBM Models

Models Function of the model

IBM Model 1 lexical translation

IBM Model 2 adds absolute reordering model

IBM Model 3 adds fertility model

IBM Model 4 adds relative alignment model

IBM Model 5 fixes deficiency

IBM Model 1

Source Document Target Document

das Haus the house

das Buch the book

ein Buch a book

Training data:

IBM Model 1

e f Initial 1st iter. 2nd iter. 3rd iter. ... Final the das 0.25 1 book das 0.25 0 house das 0.25 0 the buch 0.25 0 book buch 0.25 1 a buch 0.25 0 book ein 0.25 0 a ein 0.25 1 the haus 0.25 0 house haus 0.25 1

das haus das buch ein buch

the house the book a book

IBM Model 1

e f Initial 1st iter. 2nd iter. 3rd iter. ... Final the das 0.25 0.50 ... 1 book das 0.25 0.25 ... 0 house das 0.25 0.25 ... 0 the buch 0.25 0.25 ... 0 book buch 0.25 0.50 ... 1 a buch 0.25 0.25 ... 0 book ein 0.25 0.50 ... 0 a ein 0.25 0.50 ... 1 the haus 0.25 0.50 ... 0 house haus 0.25 0.50 ... 1



IBM Model 1

e f Initial 1st iter. 2nd iter. 3rd iter. ... Final the das 0.25 0.50 0.6364 ... 1 book das 0.25 0.25 0.1818 ... 0 house das 0.25 0.25 0.1818 ... 0 the buch 0.25 0.25 0.1818 ... 0 book buch 0.25 0.50 0.6364 ... 1 a buch 0.25 0.25 0.1818 ... 0 book ein 0.25 0.50 0.4286 ... 0 a ein 0.25 0.50 0.5714 ... 1 the haus 0.25 0.50 0.4286 ... 0 house haus 0.25 0.50 0.5714 ... 1



IBM Model 1

e f Initial 1st iter. 2nd iter. 3rd iter. ... Final the das 0.25 0.50 0.6364 0.7479 ... 1 book das 0.25 0.25 0.1818 0.1208 ... 0 house das 0.25 0.25 0.1818 0.1313 ... 0 the buch 0.25 0.25 0.1818 0.1208 ... 0 book buch 0.25 0.50 0.6364 0.7479 ... 1 a buch 0.25 0.25 0.1818 0.1313 ... 0 book ein 0.25 0.50 0.4286 0.3466 ... 0 a ein 0.25 0.50 0.5714 0.6534 ... 1 the haus 0.25 0.50 0.4286 0.3466 ... 0 house haus 0.25 0.50 0.5714 0.6534 ... 1



IBM Model 1

e f Initial 1st iter. 2nd iter. 3rd iter. ... Final the das 0.25 0.50 0.6364 0.7479 ... 1 book das 0.25 0.25 0.1818 0.1208 ... 0 house das 0.25 0.25 0.1818 0.1313 ... 0 the buch 0.25 0.25 0.1818 0.1208 ... 0 book buch 0.25 0.50 0.6364 0.7479 ... 1 a buch 0.25 0.25 0.1818 0.1313 ... 0 book ein 0.25 0.50 0.4286 0.3466 ... 0 a ein 0.25 0.50 0.5714 0.6534 ... 1 the haus 0.25 0.50 0.4286 0.3466 ... 0 house haus 0.25 0.50 0.5714 0.6534 ... 1



IBM Model 1



buch book 0.9933 a 0.0046 the 0.0020 haus house 0.9172

the 0.0827

das the 0.9933 house 0.0046 book 0.0020 ein a 0.9172

book 0.0827

Lexical (word) probabilities (after 10 iterations):

IBM Model 1



ein buch a book 0.25 book book 0.01 das buch the book 0.25

das haus the house 0.25 the the 0.01

Decoding (translating) using the lexical probabilities:

IBM Model 1



ein buch a book 0.25 book book 0.01 das buch the book 0.25

das haus the house 0.25 the the 0.01 ein haus a house 0.25 book house 0.01

Decoding (translating) using the lexical probabilities:

Language Ambiguity in SMT

schlechte6 bank2

bad6 bench4 0.1239

bad6 bank2 0.1239

Source language Target language

freundliche1 bank2 friendly1 bank2

gemütliche3 bank4 cosy3 bench4

freundliche1 friendly1

gemütliche5 cosy5

schlechte6 bad6

Generic Models in SMT

schlechte6 bank2

bad6 bench4 0.1918

bad6 bank2 0.0581





gemütliche5 cosy5

schlechte6 bad6

grüne7 bank4 green7 bench4

Domain-Specific Models in SMT





gemütliche5 cosy5

schlechte6 bad6

multinationale7 bank2 multinational7 bank2

schlechte6 bank2

bad6 bank2 0.1918

bad6 bench4 0.0581

• Maria, Mary • no, did not • slap, daba una bofetada • a la, the • bruja, witch • verde, green

• Maria no, Mary did not • no daba una bofetada, did not slap • daba una bofetada a la, slap the • bruja verde, green witch

• Maria no daba una bofetada, Mary did not slap • no daba una bofetada a la, did not slap the • a la bruja verde, the green witch

From Word to Phrase Based SMT

Generic Models in SMT

freundliche1 bank2

friendly1 bench4 0.1576

friendly1 bank2 0.0581





gemütliche5 cosy5

schlechte6 bad6

grüne7 bank4 green7 bench4

Phrase Based-SMT

schlechte _bank1

bad_bank1 1


schlechte_bank1 bad_bank1

gemütliche_bank2 cosy_bench2


gemütliche4 cosy4

schlechte5 bad5

grüne_bank6 green_bench6

Prime Minister Ayrault said: "It's incredible that an allied country like the United States at this point goes as far as spying on private communications that have no strategic justification, no justification on the basis of national defence.“

Premierminister | ayrault | sagte: |"es | ist unglaublich |, dass eine | verbündete | Land wie die | Vereinigten Staaten | an diesem Punkt | geht so | weit wie | Spionage | auf private | Mitteilungen | , dass | keine strategische |Gründe | , keine | Begründung | auf der Grundlage der nationalen | Verteidigung. |"

Why are phrases better?

Best translation = probability of Translation Model * Language Model1

(1) plus other things

Decoding - Finding the best path

Decoding - Finding the best path

he 's not home -3.09794 he 's not home . -3.26325 he is not home -3.27113 he can 't go home -3.32145 he 's not home , -3.48073 he is not to go home -3.48158 he is not home . -3.48415 he won 't go home -3.5298

it is not home -3.55166 he 's not going home -3.57796 - he 's not home -3.58466 he is not go home -3.59997 he 's not at home -3.62157 he is not going home -3.63351 he can 't go home . -3.6497 he 's not go home -3.65487

Outline

• Statistical Machine Translation (SMT)

– Insights into SMT

• Word alignment

• Word alignment -> Phrase-Based SMT

• Ontology Translation and SMT

– Domain adaptation, Term Identification

– UNLP SMT Demos:

• TeTra, OTTO, Iris

SMT

decoder @de

@nl

@it @en

@es

Terminological Injection into SMT

Changes in equity attributable to owners of parent

Änderungen im Eigenkapital , das den Eigentümern des Mutterunternehmens zuzurechnen ist

Änderungen im Eigenkapital, das den Eigentümern des Mutterunter Google Translate:

TeTra – Term Translation System

http://server1.nlp.insight-centre.org/tetra/






Translation Model

Source Term

Target Term

Generic Equity1 And2

Liabilities3

Gerechtigkeit1a und2 Verbindlichkeiten3

Financial Eigenkapital1b und2

Schulden3

Financial Domain (IFRS Ontology)

1a) Something that is just and fair. 1b) Ownership interest in a corporation, property, or other holding






Translation Model

Source Term

Target Term

Generic Birth1 injury2 to3

scalp4

Geburt1 schädigung2 zu3 skalpieren4

Medical Verletzung2 auf3 der3

Kopfhaut4

Medical Domain (ICD Ontology)




OTTO – OnTology TranslatiOn System

http://server1.nlp.insight-centre.org/otto/






OTTO – OnTology TranslatiOn System








IRIS – English-Irish Translation System

http://server1.nlp.insight-centre.org/iris/























Statistical Machine Translation for domain-specific vocabulary translation

Mihael Arčan

[email protected]

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Motivation on Ontology Translation · Motivation on Ontology Translation Business Information query in English fixed assets@en . ... Staaten | an diesem Punkt | geht so | weit wie