Making large-scale support vector machine learning practical
Advances in kernel methods
A program for aligning sentences in bilingual corpora
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
A machine learning approach to the automatic evaluation of machine translation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Linguistic features for automatic evaluation of heterogenous MT systems
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Probabilistic finite state machines for regression-based MT evaluation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
SPEDE: probabilistic edit distance metrics for MT evaluation
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
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Machine learning methods have been extensively employed in developing MT evaluation metrics and several studies show that it can help to achieve a better correlation with human assessments. Adopting the regression SVM framework, this paper discusses the linguistic motivated feature formulation strategy. We argue that "blind" combination of available features does not yield a general metrics with high correlation rate with human assessments. Instead, certain simple intuitive features serve better in establishing the regression SVM evaluation model. With six features selected, we show evidences to support our view through a few experiments in this paper.