  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 humans, gender bias can also be found in MT. For
instance when occupations are translated from a language like English, which
mostly uses gender neutral words, to a language like German, which mostly uses
a feminine and a masculine version for an occupation, a decision must be made
by the MT System. Recent research showed that MT systems are biased towards
stereotypical translation of occupations. In 2019 the first, and so far only,
challenge set, explicitly designed to measure the extent of gender bias in MT
systems has been published. In this set measurement of gender bias is solely
based on the translation of occupations. In this paper we present an extension
of this challenge set, called WiBeMT, with gender-biased adjectives and adds
sentences with gender-biased verbs. The resulting challenge set consists of
over 70, 000 sentences and has been translated with three commercial MT
systems: DeepL Translator, Microsoft Translator, and Google Translate. Results
show a gender bias for all three MT systems. This gender bias is to a great
extent significantly influenced by adjectives and to a lesser extent by verbs.
