tailieunhanh - Báo cáo khoa học: "Perplexity Minimization for Translation Model Domain Adaptation in Statistical Machine Translation"

We investigate the problem of domain adaptation for parallel data in Statistical Machine Translation (SMT). While techniques for domain adaptation of monolingual data can be borrowed for parallel data, we explore conceptual differences between translation model and language model domain adaptation and their effect on performance, such as the fact that translation models typically consist of several features that have different characteristics and can be optimized separately. We also explore adapting multiple (4–10) data sets with no a priori distinction between in-domain and out-of-domain data except for an in-domain development set. . | Perplexity Minimization for Translation Model Domain Adaptation in Statistical Machine Translation Rico Sennrich Institute of Computational Linguistics University of Zurich Binzmuhlestr. 14 CH-8050 Zurich sennrich@ Abstract We investigate the problem of domain adaptation for parallel data in Statistical Machine Translation SMT . While techniques for domain adaptation of monolingual data can be borrowed for parallel data we explore conceptual differences between translation model and language model domain adaptation and their effect on performance such as the fact that translation models typically consist of several features that have different characteristics and can be optimized separately. We also explore adapting multiple 4-10 data sets with no a priori distinction between in-domain and out-of-domain data except for an in-domain development set. 1 Introduction The increasing availability of parallel corpora from various sources welcome as it may be leads to new challenges when building a statistical machine translation system for a specific domain. The task of determining which parallel texts should be included for training and which ones hurt translation performance is tedious when performed through trial-and-error. Alternatively methods for a weighted combination exist but there is conflicting evidence as to which approach works best and the issue of determining weights is not adequately resolved. The picture looks better in language modelling where model interpolation through perplexity minimization has become a widespread method of domain adaptation. We investigate the applicability of this method for translation models and discuss possible applications. We move the focus away from a binary combination of in-domain and out-of-domain data. If we can scale up the number of models whose contributions we weight this reduces the need for a priori knowledge about the fitness1 of each potential training text and opens new research opportunities for .

• Báo cáo khoa học
• Perplexity Minimization
• Translation Model Domain Adaptation
• Statistical Machine Translation
• 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