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LORIA System for the WMT15 Quality Estimation Shared Task

Abstract : We describe our system for WMT2015 Shared Task on Quality Estimation, task 1, sentence-level prediction of post-edition effort. We use baseline features, Latent Semantic Indexing based features and features based on pseudo-references. SVM algorithm allows to estimate the linear regression between the features vectors and the HTER score. We use a selection algorithm in order to put aside needless features. Our best system leads to a performance in terms of Mean Absolute Error equal to 13.34 on official test while the official baseline system leads to a performance equal to 14.82.
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Contributor : David Langlois Connect in order to contact the contributor
Submitted on : Tuesday, January 26, 2016 - 11:28:51 AM
Last modification on : Saturday, October 16, 2021 - 11:26:08 AM


  • HAL Id : hal-01262104, version 1



David Langlois. LORIA System for the WMT15 Quality Estimation Shared Task. Workshop on Statistical Machine Translation, Sep 2015, Lisbonne, Portugal. pp.8. ⟨hal-01262104⟩



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