Gender Across Languages: Volume 4

By M. Hellinger, Heiko Motschenbacher. 2015

Subfields: Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Evaluation & Benchmarks

1. Hal Daumé Iii
2. Arturs Stafanovivcs
3. Marcis Pinnis
4. Toms Bergmanis
5. Yang Trista Cao

Citations in the corpus

• Toward Gender-Inclusive Coreference Resolution (2019, background): “For instance, a common example is the German term “Mädchen”, meaning “girl” [e.g., Hellinger and Motschenbacher 2015].”
• Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, background): “For instance, a common example is the German term “Mädchen”, meaning “girl” (e.g., Hellinger and Motschenbacher 2015).”
• Mitigating Gender Bias in Machine Translation with Target Gender Annotations (2020, background): “This is, however, a heuristic and not a rule (see Hellinger and Motschenbacher (2015) for counterexamples).”