TAILIEUCHUNG - Báo cáo khoa học: "Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars"

Syntax-based statistical machine translation (MT) aims at applying statistical models to structured data. In this paper, we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar. Synchronous dependency insertion grammars are a version of synchronous grammars defined on dependency trees. We first introduce our approach to inducing such a grammar from parallel corpora. | Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars Yuan Ding Martha Palmer Department of Computer and Information Science University of Pennsylvania Philadelphia PA 19104 USA yding mpalmer @ Abstract Syntax-based statistical machine translation MT aims at applying statistical models to structured data. In this paper we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar. Synchronous dependency insertion grammars are a version of synchronous grammars defined on dependency trees. We first introduce our approach to inducing such a grammar from parallel corpora. Second we describe the graphical model for the machine translation task which can also be viewed as a stochastic tree-to-tree transducer. We introduce a polynomial time decoding algorithm for the model. We evaluate the outputs of our MT system using the NIST and Bleu automatic MT evaluation software. The result shows that our system outperforms the baseline system based on the IBM models in both translation speed and quality. 1 Introduction Statistical approaches to machine translation pioneered by Brown et al. 1993 achieved impressive performance by leveraging large amounts of parallel corpora. Such approaches which are essentially stochastic string-to-string transducers do not explicitly model natural language syntax or semantics. In reality pure statistical systems sometimes suffer from ungrammatical outputs which are understandable at the phrasal level but sometimes hard to comprehend as a coherent sentence. In recent years syntax-based statistical machine translation which aims at applying statistical models to structural data has begun to emerge. With the research advances in natural language parsing especially the broad-coverage parsers trained from treebanks for example Collins 1999 the utilization of structural analysis of different languages has been made possible. Ideally by .

• Báo cáo khoa học
• Machine Translation
• Probabilistic Synchronous Dependency
• Insertion Grammars
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• N gram based Statistical
• Machine Translation versus Syntax Augmented Machine Translation
• comparison and system combination
• scientific reports
• model language
• process natural language
• Human translation
• Quality of translation
• Linguistic criteria
• Strengths and weaknesses
• Construction and architecture sector
• Mixing Multiple Translation Models
• Statistical Machine Translation
• SMT
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Translation Model Size Reduction
• Hierarchical Phrase based
• Translation Model Adaptation
• Monolingual Topic Information
• Balancing User Effort and Translation Error
• Interactive Machine Translation
• Confidence Measures
• Improving On line Handwritten Recognition
• Translation Models
• Multimodal Interactive Machine Translation
• Name Translation
• Learning When to Transliterate
• A CONSIDERATION ON THE CONCEPTS STRUCTURE
• LANGUAGE IN RELATION TO SELECTIONS OF TRANSLATION EQUIVALENTS OF VERBS
• MACHINE TRANSLATION SYSTEMS
• DEALING WITH INCOMPLETENESS
• LINGUISTIC KNOWLEDGE IN LANGUAGE TRANSLATION TRANSFER AND GENERATION STAGE
• MU MACHINE TRANSLATION PROJECT
• Perplexity Minimization
• Translation Model Domain Adaptation
• Improving Pronoun Translation
• Liane Guillou
• Automatic Construction
• Machine Translation Knowledge
• Translation Literalness
• Computing Consensus Translation
• Multiple Machine Translation Systems
• Enhanced Hypotheses Alignment
• A Discriminative Latent Variable Model
• A Discriminative Latent Variable Model for Statistical Machine Translation
• Machine Translation will not Work
• FORUM ON MACHINE TRANSLATION
• Martin Kay Xerox Palo Alto
• Machine Translation already does Work
• Margaret King
• Domain adaptation
• The English Vietnamese language pair
• Low resource domains
• A machine learning
• the automatic evaluation
• Sciences and humanities
• Real world oriented machine translators
• Multiple reference points
• Occupational segregation
• Khoa học máy tính
• Tạp chí khoa học
• Natural Language Processing
• Phrase based Statistical Machine Translation
• translation of russian technical literature by machine
• James W
• Perry
• Mechanical Translation
• Machine Translation Development at the University of Washington
• Erwin Reifle
• The Work on Machine Translation in the Soviet Union
• V
• Yu
• Rozentsveig
• Studies in Machine Translation—8 Manual for Postediting Russian Text
• H
• P
• Edmundson