TAILIEUCHUNG - Báo cáo khoa học: "N-gram-based Statistical Machine Translation versus Syntax Augmented Machine Translation: comparison and system combination"

In this paper we compare and contrast two approaches to Machine Translation (MT): the CMU-UKA Syntax Augmented Machine Translation system (SAMT) and UPC-TALP N-gram-based Statistical Machine Translation (SMT). SAMT is a hierarchical syntax-driven translation system underlain by a phrase-based model and a target part parse tree. In N-gram-based SMT, the translation process is based on bilingual units related to word-to-word alignment and statistical modeling of the bilingual context following a maximumentropy framework. . | N-gram-based Statistical Machine Translation versus Syntax Augmented Machine Translation comparison and system combination Maxim Khalilov and José . Fonollosa Universitat Politècnica de Catalunya Campus Nord UPC 08034 Barcelona Spain khalilov adrian @ Abstract In this paper we compare and contrast two approaches to Machine Translation MT the CMU-UKA Syntax Augmented Machine Translation system SAMT and UPC-TALP N-gram-based Statistical Machine Translation SMT . SAMT is a hierarchical syntax-driven translation system underlain by a phrase-based model and a target part parse tree. In N-gram-based SMT the translation process is based on bilingual units related to word-to-word alignment and statistical modeling of the bilingual context following a maximumentropy framework. We provide a step-by-step comparison of the systems and report results in terms of automatic evaluation metrics and required computational resources for a smaller Arabic-to-English translation task tokens in the training corpus . Human error analysis clarifies advantages and disadvantages of the systems under consideration. Finally we combine the output of both systems to yield significant improvements in translation quality. 1 Introduction There is an ongoing controversy regarding whether or not information about the syntax of language can benefit MT or contribute to a hybrid system. Classical IBM word-based models were recently augmented with a phrase translation capability as shown in Koehn et al. 2003 or in more recent implementation the MOSES MT sys-tem1 Koehn et al. 2007 . In parallel to the phrasebased approach the N-gram-based approach appeared Marino et al. 2006 . It stemms from 1 moses the Finite-State Transducers paradigm and is extended to the log-linear modeling framework as shown in Marino et al. 2006 . A system following this approach deals with bilingual units called tuples which are composed of one or more words from the source language and zero or .

• Báo cáo khoa học
• N gram based Statistical
• Machine Translation versus Syntax Augmented Machine Translation
• comparison and system combination
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln