TAILIEUCHUNG - Báo cáo khoa học: "Phrase-Based Backoff Models for Machine Translation of Highly Inflected Languages"

We propose a backoff model for phrasebased machine translation that translates unseen word forms in foreign-language text by hierarchical morphological abstractions at the word and the phrase level. The model is evaluated on the Europarl corpus for German-English and FinnishEnglish translation and shows improvements over state-of-the-art phrase-based models. | Phrase-Based Backoff Models for Machine Translation of Highly Inflected Languages Mei Yang Department of Electrical Engineering University of Washington Seattle WA USA yangmei@ Katrin Kirchhoff Department of Electrical Engineering University of Washington Seattle WA USA katrin@ Abstract We propose a backoff model for phrasebased machine translation that translates unseen word forms in foreign-language text by hierarchical morphological abstractions at the word and the phrase level. The model is evaluated on the Europarl corpus for German-English and Finnish-English translation and shows improvements over state-of-the-art phrase-based models. 1 Introduction Current statistical machine translation SMT usually works well in cases where the domain is fixed the training and test data match and a large amount of training data is available. Nevertheless standard SMT models tend to perform much better on languages that are morphologically simple whereas highly inflected languages with a large number of potential word forms are more problematic particularly when training data is sparse. SMT attempts to find a sentence ê in the desired output language given the corresponding sentence f in the source language according to e argmaxe P f e P e 1 Most state-of-the-art SMT adopt a phrase-based approach such that e is chunked into I phrases e1 . ẽ and the translation model is defined over mappings between phrases in e and in f . . P f e . Typically phrases are extracted from a word-aligned training corpus. Different inflected forms of the same lemma are treated as different words and there is no provision for unseen forms . unknown words encountered in the test data are not translated at all but appear verbatim in the output. Although the percentage of such unseen word forms may be negligible when the training set is large and matches the test set well it may rise drastically when training data is limited or from a different domain. Many .

• Báo cáo khoa học
• Phrase Based Backoff Models
• Machine Translation
• Highly Inflected Languages
• 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