Review Article: Example-based Machine Translation
Machine Translation
Models of translational equivalence among words
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
A simple hybrid aligner for generating lexical correspondences in parallel texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A probability model to improve word alignment
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
Optimizing synonym extraction using monolingual and bilingual resources
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
| Hi-index | 0.00 |
The main problems of statistical word alignment lie in the facts that source words can only be aligned to one target word, and that the inappropriate target word is selected because of data sparseness problem. This paper proposes an approach to improve statistical word alignment with a rule-based translation system. This approach first uses IBM statistical translation model to perform alignment in both directions (source to target and target to source), and then uses the translation information in the rule-based machine translation system to improve the statistical word alignment. The improved alignments allow the word(s) in the source language to be aligned to one or more words in the target language. Experimental results show a significant improvement in precision and recall of word alignment.