Access the full text NOT AVAILABLE Lookup at Google Scholar Save as. (Rural Development Administration, Suwon (Korea R.). They said the Translated Wikipedia Biographies dataset can be used to evaluate gender bias in MT output along common translation errors - among which the researchers singled out three, pro-drop, possessives, and gender agreement. Studies on ecology and control of the azuki bean weevil, Callosobruchus chinensis (L.), in Korea 1990 Han, E.D. These often occur when articles refer to a person explicitly in early sentences of a paragraph, but there is no explicit mention of the person in later sentences,” the researchers said in a Jblog post. “Because they are well-written, geographically diverse, contain multiple sentences, and refer to subjects in the third person (and so contain plenty of pronouns), Wikipedia biographies offer a high potential for common translation errors associated with gender. The ultimate goal, according to Google researchers, is to improve machine learning systems focused on gender and pronouns in translation by coming up with a benchmark for accuracy. In its ongoing quest to reduce gender bias in machine translation (MT), Google has released a dataset of Translated Wikipedia Biographies.
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