Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: I want to understand gender bias of ELMo's contextualized word embeddings and how to mitigate

Keyphrases: coreference resolution text paragraph-level

Dataset description:
WinoGrande: WinoGrande is a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations.
StereoSet: A large-scale natural dataset in English to measure stereotypical biases in four domains: gender, profession, race, and religion.
ETH Py150 Open: A massive, deduplicated corpus of 7.4M Python files from GitHub.
WinoBias: WinoBias contains 3,160 sentences, split equally for development and test, created by researchers familiar with the project. Sentences were created to follow two prototypical templates but annotators were encouraged to come up with scenarios where entities could be interacting in plausible ways.

Options:- A: WinoGrande  B: StereoSet  C: ETH Py150 Open  D: WinoBias
WinoBias