6 citations · 24 across the 8 of their papers we have counts for
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cs.CL2021★ 6 cited
Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives
Jonas-Dario Troles, Ute Schmid
Human gender bias is reflected in language and text production. Because state-of-the-art machine translation (MT) systems are trained on large corpora of text, mostly generated by…
cs.LG2021
Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach
Johannes Rabold, Michael Siebers, Ute Schmid
In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or…