The nature of statistical learning theory
The nature of statistical learning theory
A systematic comparison of various statistical alignment models
Computational Linguistics
Three heads are better than one
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Is it harder to parse Chinese, or the Chinese Treebank?
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
Multi-engine machine translation with an open-source decoder for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Using Moses to integrate multiple rule-based machine translation engines into a hybrid system
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Bridging SMT and TM with translation recommendation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Better Arabic parsing: baselines, evaluations, and analysis
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Integrating N-best SMT outputs into a TM system
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Meteor 1.3: automatic metric for reliable optimization and evaluation of machine translation systems
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
| Hi-index | 0.00 |
We present a Machine-Learning-based framework for hybrid Machine Translation. Our approach combines translation output from several black-box source systems. We define an extensible, total order on translation output and use this to decompose the n-best translations into pairwise system comparisons. Using joint, binarised feature vectors we train an SVM-based classifier and show how its classification output can be used to generate hybrid translations on the sentence level. Evaluations using automated metrics shows promising results. An interesting finding in our experiments is the fact that our approach allows to leverage good translations from otherwise bad systems as the combination decision is taken on the sentence instead of the corpus level. We conclude by summarising our findings and by giving an outlook to future work, e.g., on probabilistic classification or the integration of manual judgements.