Results from the ML4HMT-12 Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation

机译：ML4HMT-12共享任务的结果：应用机器学习技术优化混合机器翻译中的分工

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We describe the second edition of the ML4HMT shared task which challenges participants to create hybrid translations from the translation output of several individual MT systems. We provide an overview of the shared task and the data made available to participants before briefly describing the individual systems. We report on the results using automatic evaluation metrics and conclude with a summary of ML4HMT-12 and an outlook to future work.

机译：我们描述了ML4HMT共享任务的第二版，该任务挑战参与者从几个单独的MT系统的翻译输出中创建混合翻译。在简要描述各个系统之前，我们概述了共享任务和提供给参与者的数据。我们使用自动评估指标报告结果，并总结ML4HMT-12和对未来工作的展望。

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来源
《Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT》|2012年|85-90|共6页
会议地点 Mumbai(IN)
作者
Christian FEDERMANN; Tsuyoshi OKITA; Maite MELERO; Marta R. COSTA-JUSSA; Toni BADIA; Josef VAN GENABITH;
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作者单位

DFKI GmbH, Language Technology Lab, Stuhlsatzenhausweg 3, D-66123 Saarbruecken, GERMANY;

Dublin City University, School of Computing, Glasnevin, Dublin 9, IRELAND;

Barcelona Media, Speech and Language Group, Diagonal 177, 08018 Barcelona, SPAIN;

Barcelona Media, Speech and Language Group, Diagonal 177, 08018 Barcelona, SPAIN;

Barcelona Media, Speech and Language Group, Diagonal 177, 08018 Barcelona, SPAIN;

Dublin City University, School of Computing, Glasnevin, Dublin 9, IRELAND;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Machine Translation; System Combination; Machine Learning;

机译：机器翻译；系统组合；机器学习;

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机译：mL4HmT-12的共同任务是应用机器学习技术优化混合机器翻译中的分工

Results from the ML4HMT-12 Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation

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