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Empirical Machine Translation and its Evaluation

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EAMT Best Thesis Award 2008

Jes´us Gim´enez

(Advisor, Llu´ıs M`arquez)

Universitat Polit`ecnica de Catalunya

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A brilliant play written by William Locke Una obra brillante escrita por William Locke

vs. “A brilliant play by Lionel Messi that produced a wonderful goal” → “Una brillante jugada de Lionel Messi que result´o en un bello gol”

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A brilliant play written by William Locke

Una obra brillante escrita por William Locke

vs. “A brilliant play by Lionel Messi that produced a wonderful goal” → “Una brillante jugada de Lionel Messi que result´o en un bello gol”

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A brilliant play written by William Locke

Una obra brillante escrita por William Locke

vs. “A brilliant play by Lionel Messi that produced a wonderful goal” → “Una brillante jugada de Lionel Messi que result´o en un bello gol”

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A brilliant play written by William Locke

Una obra brillante escrita por William Locke

vs. “Abrilliant playby Lionel Messi that produced a wonderful goal” → “Unabrillante jugadade Lionel Messi que result´o en un bello gol”

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A brilliant play written by William Locke

Una obra brillante escrita por William Locke

vs. “Abrilliant playby Lionel Messi that produced a wonderful goal” → “Unabrillante jugadade Lionel Messi que result´o en un bello gol”

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A brilliant play written by William Locke

Una obra brillante escrita por William Locke

vs. “Abrilliant playby Lionel Messi that produced a wonderful goal” → “Unabrillante jugadade Lionel Messi que result´o en un bello gol”

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Idea

Use discriminative Machine Learning techniques in SMT to estimate P(ei|fj), actually, P(ei|fj,context fj)

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Idea

Use discriminative Machine Learning techniques in SMT to estimate P(ei|fj), actually, P(ei|fj,context fj)

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1 Discriminative Phrase Selection for SMT Shallow-syntactic features

word, lemma, parts of speech, chunking local / global context

→ Improved translation quality 2 Domain Dependence in SMT

Parliament proceedings → Dictionary definitions Adaptation based on:

EuroWordNet out-of-domain data

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1 Discriminative Phrase Selection for SMT Shallow-syntactic features

word, lemma, parts of speech, chunking local / global context

→ Improved translation quality

2 Domain Dependence in SMT

Parliament proceedings → Dictionary definitions Adaptation based on:

EuroWordNet out-of-domain data

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1 Discriminative Phrase Selection for SMT Shallow-syntactic features

word, lemma, parts of speech, chunking local / global context

→ Improved translation quality 2 Domain Dependence in SMT

Parliament proceedings → Dictionary definitions Adaptation based on:

EuroWordNet out-of-domain data

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1 Discriminative Phrase Selection for SMT Shallow-syntactic features

word, lemma, parts of speech, chunking local / global context

→ Improved translation quality 2 Domain Dependence in SMT

Parliament proceedings → Dictionary definitions Adaptation based on:

EuroWordNet out-of-domain data

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Candidate On Tuesday several missiles and mortar Translation shells fell in southern Israel , but there

were no casualties .

Reference Several Qassam rockets and mortar shells Translation fell today, Tuesday , in southern Israel

without causing any casualties .

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Candidate On Tuesday several missiles and mortar Translation shells fell in southern Israel , but there

were no casualties .

Reference Several Qassam rockets and mortar shells Translation fell today, Tuesday , in southern Israel

without causing any casualties .

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Candidate On Tuesday several missiles and mortar

Translation shells fell in southern Israel , but there were no casualties .

Reference Several Qassam rockets and mortar shells

Translation fell today, Tuesday , in southern Israel without causing any casualties .

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Idea

Define similarity measures based on deeper linguistic information

Compare linguistic structures and their lexical realizations

Linguistic levels

Syntax

Parts-of-speech Base phrase chunks Phrase constituents Dependency relationships Semantics

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S PPTMP S . On NP NPA1 VP , but S Tuesday several missiles and mortar shells fell PPLOC NP VP in NP there were NP

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S PPTMP S . On NP NPA1 VP , but S Tuesday several missiles and mortar shells fell PPLOC NP VP in NP there were NP

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S

NPA1 A0 VP .

NP and NP fell NP PPLOC PPADV

Several Qassam rockets mortar shells NPTMP , NP , in NP without S

today Tuesday southern Israel

VP

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S

NPA1A0 VP .

NP and NP fell NP PPLOC PPADV

Several Qassam rockets mortar shells NPTMP , NP , in NP without S

today Tuesday southern Israel

VP

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1 Linguistic measures providemore reliable rankings when the systems under evaluation are based on different paradigms (statistical vs. rule-based, fully-automatic vs. human-aided) 2 Linguistic measures have proven effective in shared tasks

(WMT 2007-2009) both at the system and sentence levels 3 Some linguistic measures suffer a substantial quality decrease at

the sentence level (due to parsing errors!)

4 Lexical and Linguistic measures are complementary

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1 Linguistic measures provide more reliable rankings when the systems under evaluation are based on different paradigms (statistical vs. rule-based, fully-automatic vs. human-aided) 2 Linguistic measures have proveneffective in shared tasks

(WMT 2007-2009) both at the system and sentence levels 3 Some linguistic measures suffer a substantial quality decrease at

the sentence level (due to parsing errors!)

4 Lexical and Linguistic measures are complementary

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1 Linguistic measures provide more reliable rankings when the systems under evaluation are based on different paradigms (statistical vs. rule-based, fully-automatic vs. human-aided) 2 Linguistic measures have proven effective in shared tasks

(WMT 2007-2009) both at the system and sentence levels 3 Some linguistic measures suffer a substantial quality decrease at

the sentence level (due to parsing errors!)

4 Lexical and Linguistic measures are complementary

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1 Linguistic measures provide more reliable rankings when the systems under evaluation are based on different paradigms (statistical vs. rule-based, fully-automatic vs. human-aided) 2 Linguistic measures have proven effective in shared tasks

(WMT 2007-2009) both at the system and sentence levels 3 Some linguistic measures suffer a substantial quality decrease at

the sentence level (due to parsing errors!)

4 Lexical and Linguistic measures are complementary → suitable for being combined!

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1 Linguistic measures provide more reliable rankings when the systems under evaluation are based on different paradigms (statistical vs. rule-based, fully-automatic vs. human-aided) 2 Linguistic measures have proven effective in shared tasks

(WMT 2007-2009) both at the system and sentence levels 3 Some linguistic measures suffer a substantial quality decrease at

the sentence level (due to parsing errors!)

4 Lexical and Linguistic measures are complementary → suitable for being combined!

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1 Spanish Government

Ministry of Science and Technology Ministry of Education

2 Organizers and participants of the NIST, WMT and IWSLT Evaluation Campaigns

3 A number of NLP researchers worldwide sharing their software 4 Enrique Amig´o and German Rigau

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1 Spanish Government

Ministry of Science and Technology Ministry of Education

2 Organizers and participants of the NIST, WMT and IWSLT Evaluation Campaigns

3 A number of NLP researchers worldwide sharing their software 4 Enrique Amig´o and German Rigau

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1 Spanish Government

Ministry of Science and Technology Ministry of Education

2 Organizers and participants of the NIST, WMT and IWSLT Evaluation Campaigns

3 A number of NLP researchers worldwide sharing their software 4 Enrique Amig´o and German Rigau

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1 Spanish Government

Ministry of Science and Technology Ministry of Education

2 Organizers and participants of the NIST, WMT and IWSLT Evaluation Campaigns

3 A number of NLP researchers worldwide sharing their software 4 Enrique Amig´o and German Rigau

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EAMT Best Thesis Award 2008

References

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