Foundations of statistical natural language processing
Foundations of statistical natural language processing
A vector space model for automatic indexing
Communications of the ACM
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
ORANGE: a method for evaluating automatic evaluation metrics for machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
HLT '02 Proceedings of the second international conference on Human Language Technology Research
(Meta-) evaluation of machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Hi-index | 0.00 |
This work introduces AM-FM, a semantic framework for machine translation evaluation. Based upon this framework, a new evaluation metric, which is able to operate without the need for reference translations, is implemented and evaluated. The metric is based on the concepts of adequacy and fluency, which are independently assessed by using a cross-language latent semantic indexing approach and an n-gram based language model approach, respectively. Comparative analyses with conventional evaluation metrics are conducted on two different evaluation tasks (overall quality assessment and comparative ranking) over a large collection of human evaluations involving five European languages. Finally, the main pros and cons of the proposed framework are discussed along with future research directions.