Abstract
The paper presents experiments with active learning methods for the acquisition of training data in the context of machine translation. We propose a confidence-based method which is superior to the state-of-the-art method both in terms of quality and complexity. Additionally, we discovered that oracle selection techniques that use real quality scores lead to poor results, making the effectiveness of confidence-driven methods of active learning for machine translation questionable.
Cite
CITATION STYLE
Logacheva, V., & Specia, L. (2014). Confidence-based Active Learning Methods for Machine Translation. In EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Workshop on Humans and Computer-assisted Translation, HaCaT 2014 (pp. 78–83). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w14-0312
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