  Gender bias is highly impacting natural language processing applications.
Word embeddings have clearly been proven both to keep and amplify gender biases
that are present in current data sources. Recently, contextualized word
embeddings have enhanced previous word embedding techniques by computing word
vector representations dependent on the sentence they appear in.
  In this paper, we study the impact of this conceptual change in the word
embedding computation in relation with gender bias. Our analysis includes
different measures previously applied in the literature to standard word
embeddings. Our findings suggest that contextualized word embeddings are less
biased than standard ones even when the latter are debiased.
