Machine Translation: 12th China Workshop, CWMT 2016, Urumqi, by Muyun Yang, Shujie Liu
By Muyun Yang, Shujie Liu
This e-book constitutes the refereed lawsuits of the twelfth China Workshop on laptop Translation, CWMT 2016, held in Urumqi, China, in August 2016.
the ten English papers awarded during this quantity have been rigorously reviewed and chosen from seventy six submissions. They care for statistical desktop translation, hybrid laptop translation, computer translation assessment, put up modifying, alignment, and inducing bilingual wisdom from corpora.
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Additional info for Machine Translation: 12th China Workshop, CWMT 2016, Urumqi, China, August 25–26, 2016, Revised Selected Papers
In general, when training data has closer domain to the test data, higher quality of sentences alignment and bigger scale of sentences pairs, more accurate translation rules will be learned and translation system will be more robust. In practice, higher quality and bigger scale of training data often results in its complex resources and diverse themes, which are usually different from the test data and lead to domain adaptive problem. The goal of domain adaptation in SMT is ﬁltering and devising training data, or designing and adjusting translation model, so that SMT system can generate translation results with more domain properties.
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Multi-task sequence to sequence learning (2015a). 06114 Daxiang Dong, Hua Wu, Wei He, Dianhai Yu, and Haifeng Wang. 2015. Multi-task learning for multiple language translation. : Learning phrase representations using RNN encoder-decoder for statistical machine translation. : A framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data. J. : Machine translation by triangulation: Making effective use of multi-parallel corpora. : Revisiting pivot language approach for machine translation.