Machine Translation Quality Estimation: Applications and Future Perspectives
作者： Lucia Specia , Kashif Shah
DOI: 10.1007/978-3-319-91241-7_10
关键词:
摘要: Predicting the quality of machine translation (MT) output is a topic that has been attracting significant attention. By automatically distinguishing bad from good translations, it potential to make MT more useful in number applications. In this chapter we review various practical applications where estimation (QE) at sentence level shown positive results: filtering low cases post-editing, selecting best system when multiple options are available, improving performance by additional parallel data, and sampling for assurance humans. Finally, discuss QE other levels (word document) general challenges field, as well perspectives novel directions
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