Translation Quality and Effort: Options versus Post-editing
@inproceedings{Sturgeon2015TranslationQA, title={Translation Quality and Effort: Options versus Post-editing}, author={Donald Sturgeon and John Sie Yuen Lee}, booktitle={ICON}, year={2015} }
Past research has shown that various types of computer assistance can reduce translation effort and improve translation quality over manual translation. This paper directly compares two common assistance types – selection from lists of translation options, and postediting of machine translation (MT) output produced by Google Translate – across two significantly different subject domains for Chinese-to-English translation. In terms of translation effort, we found that the use of options can…
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Preference learning for machine translation
- Computer Science
- 2018
Algorithms that can learn from very large amounts of data by exploiting pairwise preferences defined over competing translations are developed, which can be used to make a machine translation system robust to arbitrary texts from varied sources, but also enable it to learn effectively to adapt to new domains of data.
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