tailieunhanh - Báo cáo khoa học: "Computing Lattice BLEU Oracle Scores for Machine Translation"

The search space of Phrase-Based Statistical Machine Translation (PBSMT) systems can be represented under the form of a directed acyclic graph (lattice). The quality of this search space can thus be evaluated by computing the best achievable hypothesis in the lattice, the so-called oracle hypothesis. For common SMT metrics, this problem is however NP-hard and can only be solved using heuristics. In this work, we present two new methods for efficiently computing BLEU oracles on lattices: the first one is based on a linear approximation of the corpus BLEU score and is solved using the FST formalism; the second. | Computing Lattice BLEU Oracle Scores for Machine Translation Artem Sokolov Guillaume Wisniewski Francois Yvon LIMSI-CNRS Univ. Paris Sud BP-133 91 403 Orsay France @ Abstract The search space of Phrase-Based Statistical Machine Translation PBSMT systems can be represented under the form of a directed acyclic graph lattice . The quality of this search space can thus be evaluated by computing the best achievable hypothesis in the lattice the so-called oracle hypothesis. For common SMT metrics this problem is however NP-hard and can only be solved using heuristics. In this work we present two new methods for efficiently computing BLEU oracles on lattices the first one is based on a linear approximation of the corpus BLEU score and is solved using the FST formalism the second one relies on integer linear programming formulation and is solved directly and using the Lagrangian relaxation framework. These new decoders are positively evaluated and compared with several alternatives from the literature for three language pairs using lattices produced by two PBSMT systems. 1 Introduction The search space of Phrase-Based Statistical Machine Translation PBSMT systems has the form of a very large directed acyclic graph. In several softwares an approximation of this search space can be outputted either as a n-best list containing the n top hypotheses found by the decoder or as a phrase or word graph lattice which compactly encodes those hypotheses that have survived search space pruning. Lattices usually contain much more hypotheses than n-best lists and better approximate the search space. Exploring the PBSMT search space is one of the few means to perform diagnostic analysis and to better understand the behavior of the system Turchi et al. 2008 Auli et al. 2009 . Useful diagnostics are for instance provided by looking at the best oracle hypotheses contained in the search space those hypotheses that have the highest quality score with respect to .

• Báo cáo khoa học
• Computing Lattice BLEU Oracle Scores
• Machine Translation
• Artem Sokolov
• 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