Submit Search
Upload
Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop
•
1 like
•
690 views
Association for Computational Linguistics
Follow
WAT - W15-5005 (Poster)
Read less
Read more
Education
Report
Share
Report
Share
1 of 18
Download now
Download to read offline
Recommended
Trimble
Trimble
mwratschko
Recent MIP Performance Improvements in IBM ILOG CPLEX Optimization Studio
Recent MIP Performance Improvements in IBM ILOG CPLEX Optimization Studio
IBM Decision Optimization
TenYearsCPOptimizer
TenYearsCPOptimizer
PaulShawIBM
Innovations in CPLEX performance and solver capabilities
Innovations in CPLEX performance and solver capabilities
IBM Decision Optimization
Accelerating the Development of Efficient CP Optimizer Models
Accelerating the Development of Efficient CP Optimizer Models
Philippe Laborie
CPLEX 12.5.1 remote object - June 2013
CPLEX 12.5.1 remote object - June 2013
Roland Wunderling
Venkatesh Duppada - 2017 - SeerNet at EmoInt-2017: Tweet Emotion Intensity Es...
Venkatesh Duppada - 2017 - SeerNet at EmoInt-2017: Tweet Emotion Intensity Es...
Association for Computational Linguistics
Care your Child
Care your Child
Yanbin Kong
Recommended
Trimble
Trimble
mwratschko
Recent MIP Performance Improvements in IBM ILOG CPLEX Optimization Studio
Recent MIP Performance Improvements in IBM ILOG CPLEX Optimization Studio
IBM Decision Optimization
TenYearsCPOptimizer
TenYearsCPOptimizer
PaulShawIBM
Innovations in CPLEX performance and solver capabilities
Innovations in CPLEX performance and solver capabilities
IBM Decision Optimization
Accelerating the Development of Efficient CP Optimizer Models
Accelerating the Development of Efficient CP Optimizer Models
Philippe Laborie
CPLEX 12.5.1 remote object - June 2013
CPLEX 12.5.1 remote object - June 2013
Roland Wunderling
Venkatesh Duppada - 2017 - SeerNet at EmoInt-2017: Tweet Emotion Intensity Es...
Venkatesh Duppada - 2017 - SeerNet at EmoInt-2017: Tweet Emotion Intensity Es...
Association for Computational Linguistics
Care your Child
Care your Child
Yanbin Kong
Cs231n 2017 lecture10 Recurrent Neural Networks
Cs231n 2017 lecture10 Recurrent Neural Networks
Yanbin Kong
How we sleep well at night using Hystrix at Finn.no
How we sleep well at night using Hystrix at Finn.no
Henning Spjelkavik
Matthew Marge - 2017 - Exploring Variation of Natural Human Commands to a Rob...
Matthew Marge - 2017 - Exploring Variation of Natural Human Commands to a Rob...
Association for Computational Linguistics
State of Blockchain 2017: Smartnetworks and the Blockchain Economy
State of Blockchain 2017: Smartnetworks and the Blockchain Economy
Melanie Swan
Roee Aharoni - 2017 - Towards String-to-Tree Neural Machine Translation
Roee Aharoni - 2017 - Towards String-to-Tree Neural Machine Translation
Association for Computational Linguistics
Cs231n 2017 lecture11 Detection and Segmentation
Cs231n 2017 lecture11 Detection and Segmentation
Yanbin Kong
Deep Learning for Chatbot (1/4)
Deep Learning for Chatbot (1/4)
Jaemin Cho
Deep Learning in practice : Speech recognition and beyond - Meetup
Deep Learning in practice : Speech recognition and beyond - Meetup
LINAGORA
Recommender Systems, Matrices and Graphs
Recommender Systems, Matrices and Graphs
Roelof Pieters
Hackathon 2014 NLP Hack
Hackathon 2014 NLP Hack
Roelof Pieters
Visual-Semantic Embeddings: some thoughts on Language
Visual-Semantic Embeddings: some thoughts on Language
Roelof Pieters
Deep Learning for Chatbot (4/4)
Deep Learning for Chatbot (4/4)
Jaemin Cho
Neural Models for Document Ranking
Neural Models for Document Ranking
Bhaskar Mitra
Advanced Node.JS Meetup
Advanced Node.JS Meetup
LINAGORA
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
Association for Computational Linguistics
Using Text Embeddings for Information Retrieval
Using Text Embeddings for Information Retrieval
Bhaskar Mitra
Chenchen Ding - 2015 - NICT at WAT 2015
Chenchen Ding - 2015 - NICT at WAT 2015
Association for Computational Linguistics
Deep Learning & NLP: Graphs to the Rescue!
Deep Learning & NLP: Graphs to the Rescue!
Roelof Pieters
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
Association for Computational Linguistics
Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop
Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop
Association for Computational Linguistics
JVM and OS Tuning for accelerating Spark application
JVM and OS Tuning for accelerating Spark application
Tatsuhiro Chiba
Pangeanic Taus Presentation 13.06.17
Pangeanic Taus Presentation 13.06.17
Garth Brian Hedenskog
More Related Content
Viewers also liked
Cs231n 2017 lecture10 Recurrent Neural Networks
Cs231n 2017 lecture10 Recurrent Neural Networks
Yanbin Kong
How we sleep well at night using Hystrix at Finn.no
How we sleep well at night using Hystrix at Finn.no
Henning Spjelkavik
Matthew Marge - 2017 - Exploring Variation of Natural Human Commands to a Rob...
Matthew Marge - 2017 - Exploring Variation of Natural Human Commands to a Rob...
Association for Computational Linguistics
State of Blockchain 2017: Smartnetworks and the Blockchain Economy
State of Blockchain 2017: Smartnetworks and the Blockchain Economy
Melanie Swan
Roee Aharoni - 2017 - Towards String-to-Tree Neural Machine Translation
Roee Aharoni - 2017 - Towards String-to-Tree Neural Machine Translation
Association for Computational Linguistics
Cs231n 2017 lecture11 Detection and Segmentation
Cs231n 2017 lecture11 Detection and Segmentation
Yanbin Kong
Deep Learning for Chatbot (1/4)
Deep Learning for Chatbot (1/4)
Jaemin Cho
Deep Learning in practice : Speech recognition and beyond - Meetup
Deep Learning in practice : Speech recognition and beyond - Meetup
LINAGORA
Recommender Systems, Matrices and Graphs
Recommender Systems, Matrices and Graphs
Roelof Pieters
Hackathon 2014 NLP Hack
Hackathon 2014 NLP Hack
Roelof Pieters
Visual-Semantic Embeddings: some thoughts on Language
Visual-Semantic Embeddings: some thoughts on Language
Roelof Pieters
Deep Learning for Chatbot (4/4)
Deep Learning for Chatbot (4/4)
Jaemin Cho
Neural Models for Document Ranking
Neural Models for Document Ranking
Bhaskar Mitra
Advanced Node.JS Meetup
Advanced Node.JS Meetup
LINAGORA
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
Association for Computational Linguistics
Using Text Embeddings for Information Retrieval
Using Text Embeddings for Information Retrieval
Bhaskar Mitra
Chenchen Ding - 2015 - NICT at WAT 2015
Chenchen Ding - 2015 - NICT at WAT 2015
Association for Computational Linguistics
Deep Learning & NLP: Graphs to the Rescue!
Deep Learning & NLP: Graphs to the Rescue!
Roelof Pieters
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
Association for Computational Linguistics
Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop
Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop
Association for Computational Linguistics
Viewers also liked
(20)
Cs231n 2017 lecture10 Recurrent Neural Networks
Cs231n 2017 lecture10 Recurrent Neural Networks
How we sleep well at night using Hystrix at Finn.no
How we sleep well at night using Hystrix at Finn.no
Matthew Marge - 2017 - Exploring Variation of Natural Human Commands to a Rob...
Matthew Marge - 2017 - Exploring Variation of Natural Human Commands to a Rob...
State of Blockchain 2017: Smartnetworks and the Blockchain Economy
State of Blockchain 2017: Smartnetworks and the Blockchain Economy
Roee Aharoni - 2017 - Towards String-to-Tree Neural Machine Translation
Roee Aharoni - 2017 - Towards String-to-Tree Neural Machine Translation
Cs231n 2017 lecture11 Detection and Segmentation
Cs231n 2017 lecture11 Detection and Segmentation
Deep Learning for Chatbot (1/4)
Deep Learning for Chatbot (1/4)
Deep Learning in practice : Speech recognition and beyond - Meetup
Deep Learning in practice : Speech recognition and beyond - Meetup
Recommender Systems, Matrices and Graphs
Recommender Systems, Matrices and Graphs
Hackathon 2014 NLP Hack
Hackathon 2014 NLP Hack
Visual-Semantic Embeddings: some thoughts on Language
Visual-Semantic Embeddings: some thoughts on Language
Deep Learning for Chatbot (4/4)
Deep Learning for Chatbot (4/4)
Neural Models for Document Ranking
Neural Models for Document Ranking
Advanced Node.JS Meetup
Advanced Node.JS Meetup
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
Using Text Embeddings for Information Retrieval
Using Text Embeddings for Information Retrieval
Chenchen Ding - 2015 - NICT at WAT 2015
Chenchen Ding - 2015 - NICT at WAT 2015
Deep Learning & NLP: Graphs to the Rescue!
Deep Learning & NLP: Graphs to the Rescue!
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...
Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop
Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop
Similar to Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop
JVM and OS Tuning for accelerating Spark application
JVM and OS Tuning for accelerating Spark application
Tatsuhiro Chiba
Pangeanic Taus Presentation 13.06.17
Pangeanic Taus Presentation 13.06.17
Garth Brian Hedenskog
System X - About Benchmarks
System X - About Benchmarks
IBM India Smarter Computing
Improve SMT Labor Margin Per Unit
Improve SMT Labor Margin Per Unit
Damian Denis
Evaluation of MT Quality/Productivity at eBay - AMTA 2018
Evaluation of MT Quality/Productivity at eBay - AMTA 2018
Jose Luis Bonilla Sánchez
Toshiaki Nakazawa - 2015 - Over of the 2nd Workshop on Asian Translation
Toshiaki Nakazawa - 2015 - Over of the 2nd Workshop on Asian Translation
Association for Computational Linguistics
[OFW 14] Prediction of Flow Characteristics by Applying Machine Learning of S...
[OFW 14] Prediction of Flow Characteristics by Applying Machine Learning of S...
Geon-Hong Kim
Gestión proyectos traducción - Universitat Autònoma de Barcelona
Gestión proyectos traducción - Universitat Autònoma de Barcelona
Manuel Herranz
Gestión proyectos traducción en la Universitat Autònoma de Barcelona
Gestión proyectos traducción en la Universitat Autònoma de Barcelona
Manuel Herranz
Build your own statistical engines
Build your own statistical engines
Rubén Rodríguez de la Fuente
Cs 568 Spring 10 Lecture 5 Estimation
Cs 568 Spring 10 Lecture 5 Estimation
Lawrence Bernstein
Track g semiconductor test program - testinsight
Track g semiconductor test program - testinsight
chiportal
A Framework for Scene Recognition Using Convolutional Neural Network as Featu...
A Framework for Scene Recognition Using Convolutional Neural Network as Featu...
Tahmid Abtahi
Seeing the Wood for the Trees in MT Evaluation: an LSP success story from RWS
Seeing the Wood for the Trees in MT Evaluation: an LSP success story from RWS
Iconic Translation Machines
OPAL-RT and ANSYS - HIL simulation
OPAL-RT and ANSYS - HIL simulation
OPAL-RT TECHNOLOGIES
Compiler Optimization-Space Exploration
Compiler Optimization-Space Exploration
tmusabbir
2014 IEEE JAVA NETWORKING COMPUTING PROJECT Cost effective resource allocatio...
2014 IEEE JAVA NETWORKING COMPUTING PROJECT Cost effective resource allocatio...
IEEEFINALYEARSTUDENTPROJECT
IEEE 2014 JAVA NETWORKING PROJECTS Cost effective resource allocation of over...
IEEE 2014 JAVA NETWORKING PROJECTS Cost effective resource allocation of over...
IEEEGLOBALSOFTSTUDENTPROJECTS
2014 IEEE JAVA NETWORKING PROJECT Cost effective resource allocation of overl...
2014 IEEE JAVA NETWORKING PROJECT Cost effective resource allocation of overl...
IEEEFINALSEMSTUDENTSPROJECTS
9. Manuel Harranz (pangeanic) Hybrid Solutions for Translation
9. Manuel Harranz (pangeanic) Hybrid Solutions for Translation
RIILP
Similar to Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop
(20)
JVM and OS Tuning for accelerating Spark application
JVM and OS Tuning for accelerating Spark application
Pangeanic Taus Presentation 13.06.17
Pangeanic Taus Presentation 13.06.17
System X - About Benchmarks
System X - About Benchmarks
Improve SMT Labor Margin Per Unit
Improve SMT Labor Margin Per Unit
Evaluation of MT Quality/Productivity at eBay - AMTA 2018
Evaluation of MT Quality/Productivity at eBay - AMTA 2018
Toshiaki Nakazawa - 2015 - Over of the 2nd Workshop on Asian Translation
Toshiaki Nakazawa - 2015 - Over of the 2nd Workshop on Asian Translation
[OFW 14] Prediction of Flow Characteristics by Applying Machine Learning of S...
[OFW 14] Prediction of Flow Characteristics by Applying Machine Learning of S...
Gestión proyectos traducción - Universitat Autònoma de Barcelona
Gestión proyectos traducción - Universitat Autònoma de Barcelona
Gestión proyectos traducción en la Universitat Autònoma de Barcelona
Gestión proyectos traducción en la Universitat Autònoma de Barcelona
Build your own statistical engines
Build your own statistical engines
Cs 568 Spring 10 Lecture 5 Estimation
Cs 568 Spring 10 Lecture 5 Estimation
Track g semiconductor test program - testinsight
Track g semiconductor test program - testinsight
A Framework for Scene Recognition Using Convolutional Neural Network as Featu...
A Framework for Scene Recognition Using Convolutional Neural Network as Featu...
Seeing the Wood for the Trees in MT Evaluation: an LSP success story from RWS
Seeing the Wood for the Trees in MT Evaluation: an LSP success story from RWS
OPAL-RT and ANSYS - HIL simulation
OPAL-RT and ANSYS - HIL simulation
Compiler Optimization-Space Exploration
Compiler Optimization-Space Exploration
2014 IEEE JAVA NETWORKING COMPUTING PROJECT Cost effective resource allocatio...
2014 IEEE JAVA NETWORKING COMPUTING PROJECT Cost effective resource allocatio...
IEEE 2014 JAVA NETWORKING PROJECTS Cost effective resource allocation of over...
IEEE 2014 JAVA NETWORKING PROJECTS Cost effective resource allocation of over...
2014 IEEE JAVA NETWORKING PROJECT Cost effective resource allocation of overl...
2014 IEEE JAVA NETWORKING PROJECT Cost effective resource allocation of overl...
9. Manuel Harranz (pangeanic) Hybrid Solutions for Translation
9. Manuel Harranz (pangeanic) Hybrid Solutions for Translation
More from Association for Computational Linguistics
Muis - 2016 - Weak Semi-Markov CRFs for NP Chunking in Informal Text
Muis - 2016 - Weak Semi-Markov CRFs for NP Chunking in Informal Text
Association for Computational Linguistics
Castro - 2018 - A High Coverage Method for Automatic False Friends Detection ...
Castro - 2018 - A High Coverage Method for Automatic False Friends Detection ...
Association for Computational Linguistics
Castro - 2018 - A Crowd-Annotated Spanish Corpus for Humour Analysis
Castro - 2018 - A Crowd-Annotated Spanish Corpus for Humour Analysis
Association for Computational Linguistics
Muthu Kumar Chandrasekaran - 2018 - Countering Position Bias in Instructor In...
Muthu Kumar Chandrasekaran - 2018 - Countering Position Bias in Instructor In...
Association for Computational Linguistics
Daniel Gildea - 2018 - The ACL Anthology: Current State and Future Directions
Daniel Gildea - 2018 - The ACL Anthology: Current State and Future Directions
Association for Computational Linguistics
Elior Sulem - 2018 - Semantic Structural Evaluation for Text Simplification
Elior Sulem - 2018 - Semantic Structural Evaluation for Text Simplification
Association for Computational Linguistics
Daniel Gildea - 2018 - The ACL Anthology: Current State and Future Directions
Daniel Gildea - 2018 - The ACL Anthology: Current State and Future Directions
Association for Computational Linguistics
Wenqiang Lei - 2018 - Sequicity: Simplifying Task-oriented Dialogue Systems w...
Wenqiang Lei - 2018 - Sequicity: Simplifying Task-oriented Dialogue Systems w...
Association for Computational Linguistics
Chenchen Ding - 2015 - NICT at WAT 2015
Chenchen Ding - 2015 - NICT at WAT 2015
Association for Computational Linguistics
Zhongyuan Zhu - 2015 - Evaluating Neural Machine Translation in English-Japan...
Zhongyuan Zhu - 2015 - Evaluating Neural Machine Translation in English-Japan...
Association for Computational Linguistics
Zhongyuan Zhu - 2015 - Evaluating Neural Machine Translation in English-Japan...
Zhongyuan Zhu - 2015 - Evaluating Neural Machine Translation in English-Japan...
Association for Computational Linguistics
Hyoung-Gyu Lee - 2015 - NAVER Machine Translation System for WAT 2015
Hyoung-Gyu Lee - 2015 - NAVER Machine Translation System for WAT 2015
Association for Computational Linguistics
Graham Neubig - 2015 - Neural Reranking Improves Subjective Quality of Machin...
Graham Neubig - 2015 - Neural Reranking Improves Subjective Quality of Machin...
Association for Computational Linguistics
Graham Neubig - 2015 - Neural Reranking Improves Subjective Quality of Machin...
Graham Neubig - 2015 - Neural Reranking Improves Subjective Quality of Machin...
Association for Computational Linguistics
Terumasa Ehara - 2015 - System Combination of RBMT plus SPE and Preordering p...
Terumasa Ehara - 2015 - System Combination of RBMT plus SPE and Preordering p...
Association for Computational Linguistics
Terumasa Ehara - 2015 - System Combination of RBMT plus SPE and Preordering p...
Terumasa Ehara - 2015 - System Combination of RBMT plus SPE and Preordering p...
Association for Computational Linguistics
Toshiaki Nakazawa - 2015 - Overview of the 2nd Workshop on Asian Translation
Toshiaki Nakazawa - 2015 - Overview of the 2nd Workshop on Asian Translation
Association for Computational Linguistics
Hua Shan - 2015 - A Dependency-to-String Model for Chinese-Japanese SMT System
Hua Shan - 2015 - A Dependency-to-String Model for Chinese-Japanese SMT System
Association for Computational Linguistics
Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...
Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...
Association for Computational Linguistics
Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...
Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...
Association for Computational Linguistics
More from Association for Computational Linguistics
(20)
Muis - 2016 - Weak Semi-Markov CRFs for NP Chunking in Informal Text
Muis - 2016 - Weak Semi-Markov CRFs for NP Chunking in Informal Text
Castro - 2018 - A High Coverage Method for Automatic False Friends Detection ...
Castro - 2018 - A High Coverage Method for Automatic False Friends Detection ...
Castro - 2018 - A Crowd-Annotated Spanish Corpus for Humour Analysis
Castro - 2018 - A Crowd-Annotated Spanish Corpus for Humour Analysis
Muthu Kumar Chandrasekaran - 2018 - Countering Position Bias in Instructor In...
Muthu Kumar Chandrasekaran - 2018 - Countering Position Bias in Instructor In...
Daniel Gildea - 2018 - The ACL Anthology: Current State and Future Directions
Daniel Gildea - 2018 - The ACL Anthology: Current State and Future Directions
Elior Sulem - 2018 - Semantic Structural Evaluation for Text Simplification
Elior Sulem - 2018 - Semantic Structural Evaluation for Text Simplification
Daniel Gildea - 2018 - The ACL Anthology: Current State and Future Directions
Daniel Gildea - 2018 - The ACL Anthology: Current State and Future Directions
Wenqiang Lei - 2018 - Sequicity: Simplifying Task-oriented Dialogue Systems w...
Wenqiang Lei - 2018 - Sequicity: Simplifying Task-oriented Dialogue Systems w...
Chenchen Ding - 2015 - NICT at WAT 2015
Chenchen Ding - 2015 - NICT at WAT 2015
Zhongyuan Zhu - 2015 - Evaluating Neural Machine Translation in English-Japan...
Zhongyuan Zhu - 2015 - Evaluating Neural Machine Translation in English-Japan...
Zhongyuan Zhu - 2015 - Evaluating Neural Machine Translation in English-Japan...
Zhongyuan Zhu - 2015 - Evaluating Neural Machine Translation in English-Japan...
Hyoung-Gyu Lee - 2015 - NAVER Machine Translation System for WAT 2015
Hyoung-Gyu Lee - 2015 - NAVER Machine Translation System for WAT 2015
Graham Neubig - 2015 - Neural Reranking Improves Subjective Quality of Machin...
Graham Neubig - 2015 - Neural Reranking Improves Subjective Quality of Machin...
Graham Neubig - 2015 - Neural Reranking Improves Subjective Quality of Machin...
Graham Neubig - 2015 - Neural Reranking Improves Subjective Quality of Machin...
Terumasa Ehara - 2015 - System Combination of RBMT plus SPE and Preordering p...
Terumasa Ehara - 2015 - System Combination of RBMT plus SPE and Preordering p...
Terumasa Ehara - 2015 - System Combination of RBMT plus SPE and Preordering p...
Terumasa Ehara - 2015 - System Combination of RBMT plus SPE and Preordering p...
Toshiaki Nakazawa - 2015 - Overview of the 2nd Workshop on Asian Translation
Toshiaki Nakazawa - 2015 - Overview of the 2nd Workshop on Asian Translation
Hua Shan - 2015 - A Dependency-to-String Model for Chinese-Japanese SMT System
Hua Shan - 2015 - A Dependency-to-String Model for Chinese-Japanese SMT System
Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...
Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...
Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...
Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...
Recently uploaded
Science 7 - LAND and SEA BREEZE and its Characteristics
Science 7 - LAND and SEA BREEZE and its Characteristics
KarinaGenton
Alper Gobel In Media Res Media Component
Alper Gobel In Media Res Media Component
InMediaRes1
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communication
nomboosow
Class 11 Legal Studies Ch-1 Concept of State .pdf
Class 11 Legal Studies Ch-1 Concept of State .pdf
akmcokerachita
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
Marc Dusseiller Dusjagr
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory Inspection
SafetyChain Software
How to Make a Pirate ship Primary Education.pptx
How to Make a Pirate ship Primary Education.pptx
manuelaromero2013
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
Steve Thomason
9953330565 Low Rate Call Girls In Rohini Delhi NCR
9953330565 Low Rate Call Girls In Rohini Delhi NCR
9953056974 Low Rate Call Girls In Saket, Delhi NCR
Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1
Maestría en Comunicación Digital Interactiva - UNR
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Krashi Coaching
Employee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptx
NirmalaLoungPoorunde1
Separation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and Actinides
FatimaKhan178732
Presiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha elections
anshu789521
Concept of Vouching. B.Com(Hons) /B.Compdf
Concept of Vouching. B.Com(Hons) /B.Compdf
UmakantAnnand
mini mental status format.docx
mini mental status format.docx
PoojaSen20
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptx
OH TEIK BIN
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
iammrhaywood
Hybridoma Technology ( Production , Purification , and Application )
Hybridoma Technology ( Production , Purification , and Application )
Sakshi Ghasle
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptx
pboyjonauth
Recently uploaded
(20)
Science 7 - LAND and SEA BREEZE and its Characteristics
Science 7 - LAND and SEA BREEZE and its Characteristics
Alper Gobel In Media Res Media Component
Alper Gobel In Media Res Media Component
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communication
Class 11 Legal Studies Ch-1 Concept of State .pdf
Class 11 Legal Studies Ch-1 Concept of State .pdf
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory Inspection
How to Make a Pirate ship Primary Education.pptx
How to Make a Pirate ship Primary Education.pptx
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
9953330565 Low Rate Call Girls In Rohini Delhi NCR
9953330565 Low Rate Call Girls In Rohini Delhi NCR
Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Employee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptx
Separation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and Actinides
Presiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha elections
Concept of Vouching. B.Com(Hons) /B.Compdf
Concept of Vouching. B.Com(Hons) /B.Compdf
mini mental status format.docx
mini mental status format.docx
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
Hybridoma Technology ( Production , Purification , and Application )
Hybridoma Technology ( Production , Purification , and Application )
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptx
Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop
1.
© 2015 Toshiba
Corporation Toshiba MT System Description for the WAT2015 Workshop Satoshi SONOH Satoshi KINOSHITA Knowledge Media Laboratory, Corporate Research & Development Center, Toshiba Corporation. WAT 2015, Oct. 16, 2015 @ Kyoto
2.
© 2015 Toshiba
Corporation 2 Motivations • Rule-Based Machine Translation (RBMT) – We have been developed RBMT for more than 30 years. – Japanese⇔English, Japanese⇔Chinese, Japanese⇔Korean – Large technical dictionaries and translation rules • Pre-ordering SMT and Tree/Forest to String – Effective solutions for Asian language translation (WAT2014) – But, pre-ordering rules and parsers are needed. • Our approach: – Statistical Post Editing (SPE) (same as WAT2014) • Verify effectiveness in all tasks – System combination between SPE and SMT (new in WAT2015)
3.
© 2015 Toshiba
Corporation 3 Statistical Post Editing (SPE) Source Sentence RBMT Translated Sentence Target Sentence TM (ja’ -> ja) LM RBMT Input Sentence Translated Sentence SPE ResultSPE Model Parallel Corpus (ASPEC / JPC) 本发明具有以下效果。 本発明は以下効果を持っている。 本発明は以下の効果を有する。 1) We first translate source sentences by RBMT. 2) We train SPE model by translated corpus. Translating RBMT results to post-edited results.
4.
© 2015 Toshiba
Corporation 4 Features of SPE • From RBMT’s standpoint – Correct mistranslations / Translate unknown words • Phrase-level correction (domain adaptation) – Improve fluency • Use of more fluent expressions • Insertion of particles – Recover translation failure • From SMT’s standpoint – Pre-ordering by RBMT – Reduction of NULL alignment (subject/particle) – Use of syntax information (polarity/aspect) – Enhancement of lexicon SRC: 本发明 具有 以下 效果。 RBMT: 本発明 は 以下 効果 を 持っている 。 SPE: 本発明 は 以下 の 効果 を 有する 。
5.
© 2015 Toshiba
Corporation 5 SPE for Patent Translation 28.6 37.18 46.62 0 10 20 30 40 50 RBMT SMT SPE en-ja 27.19 38.6 39.95 0 10 20 30 40 50 RBMT SMT SPE zh-ja 51.4 70.57 68.71 0 10 20 30 40 50 60 70 80 RBMT SMT SPE ko-ja BLEUBLEUBLEU 0% 25% 50% 75% 100% RBMT SMT SPE Adequacy 1 2 3 4 5 0% 25% 50% 75% 100% RBMT SMT SPE Acceptability F C B A AA Human evaluation for zh-ja Corpus: JPO-NICT patent corpus # of training data: 2M(en-ja), 1M(zh-ja/ko-ja) # of automatic evaluation: 2,000 # of human evaluation: 200 Automatic evaluation for en-ja/zh-ja/ko-ja * * 39.8 38.8 43.9 en-ja zh-ja ko-ja SPE shows: - Better scores than PB-SMT in automatic evaluation - Improvements of understandable level (>=C in acceptability)
6.
© 2015 Toshiba
Corporation 6 System Combination • How combine systems? – Selection based on SMT scores and/or other features. – Selection based on estimated score (Adequacy? Fluency? …) • Need data to learn the relationship… • Our approach in WAT2015: – Merge n-best candidates and rescore them. – We used RNNLM for reranking. SMT SPE N-best candidates N-best candidates Merge and Rescore Final translation
7.
© 2015 Toshiba
Corporation 7 • Reranking on the log-linear model – Adding RNNLM score to default features of Moses. – RNNLM trained by rnnlm toolkit (Mikolov ‘12). • 500,000 sentences for each language • # of hidden layer=500, # of class=50 • Tuning – Using tuned weights without RNNLM, we ran only 1 iteration. (to reduce tuning time) Wlm=0.4 Wtrans=0.1 … Wlm=0.2 Wtrans=0.3 … Wlm=0.3 Wtrans=0.2 … Wrnnlm=0.0 RNNLM reranking and Tuning SMT SPE Dev Default features Default features Tuned weights Tuned weights New features Initial weights Linear interpolationAdding RNNLM MERT Tuned weights Wlm=0.2 Wtrans=0.3 … Wrnnlm=0.3
8.
© 2015 Toshiba
Corporation 8 Experimental Results 17.41 25.17 28.20 36.34 22.65 31.10 29.48 35.76 23.00 31.82 29.60 37.47 ja-en en-ja ja-zh zh-ja 38.77 70.17 39.01 68.47 40.23 70.4 JPOzh-ja JPOko-ja BLEU for ASPEC BLEU for Patent +0.35 +0.72 +0.12 +1.71 +1.22 +1.93 *SMT and SPE are 1-best results. SMT ja-en SPE COMB SMT en-ja SPE COMB SMT ja-zh SPE COMB SMT zh-ja SPE COMB SMT JPCzh-ja SPE COMB SMT JPCko-ja SPE COMB
9.
© 2015 Toshiba
Corporation 9 Systems Rerank JPCzh-ja JPCko-ja BLEU RIBES BLEU RIBES RBMT No 25.81 0.764 51.28 0.902 SMT No 38.77 0.802 70.17 0.943 Yes 39.18 0.805 70.89 0.944 SPE No 39.01 0.813 68.47 0.940 Yes 39.30 0.811 68.76 0.940 COMB Yes 40.23 0.813 70.40 0.942 Systems Rerank ja-en en-ja ja-zh zh-ja BLEU RIBES BLEU RIBES BLEU RIBES BLEU RIBES RBMT No 15.31 0.677 14.78 0.685 19.51 0.767 15.39 0.767 SMT No 17.41 0.620 25.17 0.642 28.20 0.810 36.34 0.810 Yes 17.85 0.619 25.37 0.643 28.46 0.809 36.69 0.809 SPE No 22.65 0.717 31.10 0.767 29.48 0.809 35.76 0.809 Yes 22.92 0.718 31.73 0.770 29.49 0.809 36.06 0.809 COMB Yes 23.00 0.716 31.82 0.770 29.60 0.810 37.47 0.810 Experimental Results System Combination (COMB) achieved improvements of BLEU and RIBES score than SPE. COMB is the best system except JPCko-ja task.
10.
© 2015 Toshiba
Corporation 10 Which systems did the combination selected? SMT 14% SPE 83% SAME 3% SMT 9% SPE 89% SAME 2% SMT 40% SPE 55% SAME 5% SMT 18% SPE 79% SAME 3% SMT 52% SPE 43% SAME 5% SMT 61% SPE 19% SAME 20% ja-en en-ja ja-zh zh-ja JPCzh-ja JPCko-ja “same” means that COMB results were included both SMT and SPE. ja-en/en-ja/zh-ja: about 80% translations come from SPE. ja-zh and JPCzh-ja: COMB selected SPE and SMT, equivalently. (Because RBMT couldn’t translate well, % of SMT increased. )
11.
© 2015 Toshiba
Corporation 11 Translation Examples SRC 还对在完成来所登记之前的各个环节进行了介绍。 REF 来所登録が完了するまでの流れ等も紹介した。 RBMT さらに完成が登記する前でのそれぞれの段階に対して紹介を行った。 SMT 通所を完了しても登録までの各環節について紹介した。 SPE 登録の完了までの各段階について紹介した。 COMB 登録が完了するまでの各段階について紹介した。 zh-ja: COMB was selected from n-best of SPE. SRC 图6是用于说明本发明第一实施例中的移动台站的活动水平控制的图; REF 図6は、本発明の第1の実施例における移動局の活動レベルの制御を説明するための図である。 RBMT 図6は、本発明の第一実施例中を説明するのに用いる移動台ステーションの活動レベリングコン トロールの図である; SMT 図6は、本発明の第1の実施例における移動局のアクティブレベル制御を示す図である。 SPE 図6は、本発明の第1の実施形態を説明するための移動局の活動水平コントロールの図である。 COMB 図6は、本発明の第1の実施例における移動局のアクティブレベル制御を示す図である。 JPCzh-ja: COMB was selected from n-best of SMT.
12.
© 2015 Toshiba
Corporation 12 Toshiba MT system of WAT2015 • We additionally applied some pre/post processing. Technical Term Dictionaries Selecting RBMT dictionaries by devset. + JPO patent dictionary (2.2M words for JPCzh-ja) English Word Correction Edited-distance based correction. continous -> continuous behvior -> behavior resolutin -> resolution KATAKANA Normalization Normalize to highly- frequent notations for “ー”. スクリュ -> スクリュー サーバー -> サーバ Post-translation Translate remaining unknown words by RBMT. アルキメデス数 ->阿基米德数 流入마하수 -> 流入マッハ数
13.
© 2015 Toshiba
Corporation 13 Official Results • SPE and SMT ranked in the top 3 HUMAN in ja-en/ja-zh/JPCzh-ja. • The correlation between BLEU/RIEBES and HUMAN is not clear in our system. System ja-en en-ja ja-zh zh-ja BLEU RIBES HUMAN BLEU RIBES HUMAN BLEU RIBES HUMAN BLEU RIBES HUMAN SPE 22.89 0.719 25.00 32.06 0.771 40.25 30.17 0.813 2.50 35.85 0.825 -1.00 COMB 23.00 0.716 21.25 31.82 0.770 - 30.07 0.817 17.00 37.47 0.827 18.00 System JPCzh-ja JPCko-ja BLEU RIBES HUMAN BLEU RIBES HUMAN SMT - - - 71.01 0.944 4.50 SPE 41.12 0.822 24.25 - - - COMB 41.82 0.821 14.50 70.51 0.942 3.00 R² = 0.2338 -10.00 0.00 10.00 20.00 30.00 40.00 50.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 R² = 0.3813 -10.00 0.00 10.00 20.00 30.00 40.00 50.00 0.700 0.750 0.800 0.850 0.900 0.950 1.000 BLEU-HUMAN RIBES-HUMAN
14.
© 2015 Toshiba
Corporation 14 Crowdsourcing Evaluation • Analysis of JPCko-ja result (COMB vs Online A) – In in-house evaluation, COMB is better than Online A. – Effected by differences in number expressions !? SRC : 시스템(100) ⇒ Online A: システム(100) COMB(SMT): システム100 ⇒ Equally evaluated in-house evaluation. – Crowd-workers should be provided an evaluation guideline by which such a difference is considered. BLEU RIBES HUMAN Baseline COMB Online A COMB 70.51 0.94 3.00 - 10.75 Online A 55.05 0.91 38.75 -10.75 - Official (Crowdsourcing) In-house evaluation results
15.
© 2015 Toshiba
Corporation 15 Findings of Crowdsourcing Evaluation (1) • Many workers evaluated more than one language pairs. – 22.4% workers evaluated all languages. – 22.4% workers evaluated 78.5% sentences. English Chinese Korean Languages of workers 10.4% 5.2% 46.9% 13.0% 0.5% 22.4% 1.6% 79% 9.1% 0.7% 10.1% 1.2% 0.5% English/Chinese/Korean English/Chinese Chinese/Korean English/Korean English Chinese Korean Ratio of sentences evaluated by workers
16.
© 2015 Toshiba
Corporation 16 0 50 100 150 200 250 300 350 2 1 0 -1 -2 SAME Different Findings of Crowdsourcing Evaluation (2) • In JPCko-ja, we got two evaluation results. – Unofficial results: evaluation of original translation (HUMAN=29.75) – Official results: evaluation of normalized translation (to full-width) Ex.) T溶融(DSC)=89.9℃;T結晶化(DSC)=72℃(5℃/分でDSCで測定)。 ⇒ T溶融(DSC)=89.9℃;T結晶化(DSC)=72℃(5℃/分でDSCで測定)。 Change of evaluation by same workers 9.2% sentences are 2Down. 65% sentences are equally evaluated. Win ⇒ Win Tie ⇒ Tie Lose ⇒ Lose Win ⇒ LoseLose ⇒ Win Tie ⇒ Win Lose ⇒ Tie Win ⇒ Tie Tie ⇒ Lose #ofsentences
17.
© 2015 Toshiba
Corporation 17 Summary • Toshiba MT system achieved a combination method between SMT and SPE by RNNLM reranking. • Our system ranked the top 3 HUMAN score in ja-en/ja- zh/JPCzh-ja. • We will aim for practical MT system by more effective combination systems (SMT, SPE , RBMT and more...)
Download now