Abstract
This paper introduces our SAU-KERC system that achieved F1 score of 0.39 in the world-level quality estimation task in WMT2015. The goal is to assign each translated word a "OK" or "BAD" label indicating translation quality. We adopt the sequence labeling model, conditional random fields (CRF), to predict the labels. Since "BAD" labels are rare in the training and development sets, recognition rate of "BAD" is low. To solve this problem, we propose two strategies. One is to replace "OK" label with sub-labels to balance label distribution. The other is to reconstruct the training set to include more "BAD" words.
Cite
CITATION STYLE
Shang, L., Cai, D., & Ji, D. (2015). Strategy-based technology for estimating mt quality. In 10th Workshop on Statistical Machine Translation, WMT 2015 at the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Proceedings (pp. 348–352). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w15-3042
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