Data Enhancement and Selection Strategies for the Word-level Quality Estimation

机译：词级质量估计的数据增强和选择策略

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This paper describes the DCU-SHEFF word-level Quality Estimation (QE) system submitted to the QE shared task at WMT15. Starting from a baseline set of features and a CRF algorithm to learn a sequence tagging model, we propose improvements in two ways: (ⅰ) by filtering out the training sentences containing too few errors, and (ⅱ) by adding incomplete sequences to the training data to enrich the model with new information. We also experiment with considering the task as a classification problem, and report results using a subset of the features with Random Forest classifiers.

机译：本文介绍了在WMT15上提交给QE共享任务的DCU-SHEFF单词级质量评估（QE）系统。从一组基线特征和一个CRF算法开始以学习序列标记模型，我们提出了两种改进方法：（ⅰ）通过过滤掉错误太少的训练语句，以及（ⅱ）在训练中添加不完整的序列数据以用新信息丰富模型。我们还尝试将任务视为分类问题，并使用带有随机森林分类器的部分功能报告结果。

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《10th Workshop on statistical machine translation 2015》|2015年|330-335|共6页
会议地点 Lisbon(PT)
作者
Varvara Logacheva; Chris Hokamp; Lucia Specia;
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作者单位

Department of Computer Science, University of Sheffield, UK;

CNGL Centre for Global Intelligent Content Dublin City University, Ireland;

Department of Computer Science, University of Sheffield, UK;

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正文语种 eng
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Data Enhancement and Selection Strategies for the Word-level Quality Estimation

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