A framework of a mechanical translation between Japanese and English by analogy principle
Proc. of the international NATO symposium on Artificial and human intelligence
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Toward memory-based translation
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Pilot implementation of a Bilingual Knowledge Bank
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Constituent boundary parsing for example-based machine translation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Overcoming the customization bottleneck using example-based MT
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
SENSEVAL-2 Japanese translation task
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Feature rich translation model for example-based machine translation
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Acquiring bilingual named entity translations from content-aligned corpora
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Example-Based machine translation without saying inferable predicate
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Hi-index | 0.00 |
We propose a method of constructing an example-based machine translation (EBMT) system that exploits a content-aligned bilingual corpus. First, the sentences and phrases in the corpus are aligned across the two languages, and the pairs with high translation confidence are selected and stored in the translation memory. Then, for a given input sentences, the system searches for fitting examples based on both the monolingual similarity and the translation confidence of the pair, and the obtained results are then combined to generate the translation. Our experiments on translation selection showed the accuracy of 85% demonstrating the basic feasibility of our approach.