A framework of a mechanical translation between Japanese and English by analogy principle
Proc. of the international NATO symposium on Artificial and human intelligence
Learning translation templates from examples
Information Systems - Special issue on selected papers from 6th annual workshop on information technologies and systems, December 1996, Cleveland, Ohio, USA
Learning Translation Templates from Bilingual Translation Examples
Applied Intelligence
Ordering Translation Templates by Assigning Confidence Factors
AMTA '98 Proceedings of the Third Conference of the Association for Machine Translation in the Americas on Machine Translation and the Information Soup
Chart-based transfer rule application in Machine Translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Synchronous tree-adjoining grammars
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Feedback cleaning of machine translation rules using automatic evaluation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Machine translation using probabilistic synchronous dependency insertion grammars
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Inducing translation templates with type constraints
Machine Translation
Image retrieval based on augmented relational graph representation
Applied Intelligence
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Example-Based Machine Translation (EBMT) is a corpus based approach to Machine Translation (MT), that utilizes the translation by analogy concept. In our EBMT system, translation templates are extracted automatically from bilingual aligned corpora by substituting the similarities and differences in pairs of translation examples with variables. In the earlier versions of the discussed system, the translation results were solely ranked using confidence factors of the translation templates. In this study, we introduce an improved ranking mechanism that dynamically learns from user feedback. When a user, such as a professional human translator, submits his evaluation of the generated translation results, the system learns "context-dependent co-occurrence rules" from this feedback. The newly learned rules are later consulted, while ranking the results of the subsequent translations. Through successive translation-evaluation cycles, we expect that the output of the ranking mechanism complies better with user expectations, listing the more preferred results in higher ranks. We also present the evaluation of our ranking method which uses the precision values at top results and the BLEU metric.