Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICTAI.2010.122
Title: Support vector methods for sentence level machine translation evaluation
Authors: Veillard, A.
Melissa, E.
Theodora, C.
Racoceanu, D.
Bressan, S. 
Issue Date: 2010
Source: Veillard, A., Melissa, E., Theodora, C., Racoceanu, D., Bressan, S. (2010). Support vector methods for sentence level machine translation evaluation. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI 2 : 347-348. ScholarBank@NUS Repository. https://doi.org/10.1109/ICTAI.2010.122
Abstract: Recent work in the field of machine translation (MT) evaluation suggests that sentence level evaluation based on machine learning (ML) can outperform the standard metrics such as BLEU, ROUGE and METEOR. We conducted a comprehensive empirical study on support vector methods for ML-based MT evaluation involving multi-class support vector machines (SVM) and support vector regression (SVR) with different kernel functions. We empathize on a systematic comparison study of multiple feature models obtained with feature selection and feature extraction techniques. Besides finding the conditions yielding the best empirical results, our study supports several unobvious conclusions regarding qualitative and quantitative aspects of feature sets in MT evaluation. © 2010 IEEE.
Source Title: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
URI: http://scholarbank.nus.edu.sg/handle/10635/41740
ISBN: 9780769542638
ISSN: 10823409
DOI: 10.1109/ICTAI.2010.122
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