Learning finite-state models for machine translation

DOI10.1007/S10994-006-9612-9zbMath1470.68052OpenAlexW2071991030MaRDI QIDQ2384129

Enrique Vidal, Francisco Casacuberta

Publication date: 20 September 2007

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-006-9612-9

zbMATH Keywords

machine translationgrammatical inferencestochastic finite-state transducers

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05) Formal languages and automata (68Q45) Grammars and rewriting systems (68Q42) Natural language processing (68T50)

Uses Software

• Thot
• BLEU

Cites Work

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• Inference of finite-state transducers from regular languages
• A Systematic Comparison of Various Statistical Alignment Models
• Word Reordering and a Dynamic Programming Beam Search Algorithm for Statistical Machine Translation
• Statistical Machine Translation with Scarce Resources Using Morpho-syntactic Information
• Machine Translation with Inferred Stochastic Finite-State Transducers
• The Alignment Template Approach to Statistical Machine Translation
• Structural, Syntactic, and Statistical Pattern Recognition
• Structural, Syntactic, and Statistical Pattern Recognition
• Grammatical Inference: Algorithms and Applications
• Some statistical-estimation methods for stochastic finite-state transducers
• Language simplification through error-correcting and grammatical inference techniques
• Unit completion for a computer-aided translation typing system
• The EuTrans-I spoken language translation system

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