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: A method for pretraining and fine-tuning multilingual language models.

Keyphrases: cross-lingual classification text

Dataset description:
MLDoc: Multilingual Document Classification Corpus (MLDoc) is a cross-lingual document classification dataset covering English, German, French, Spanish, Italian, Russian, Japanese and Chinese. It is a subset of the Reuters Corpus Volume 2 selected according to the following design choices:   uniform class coverage: same number of examples for each class and language, official train / development / test split: for each language a training data of different sizes (1K, 2K, 5K and 10K stories), a development (1K) and a test corpus (4K) are provided (with exception of Spanish and Russian with 9458 and 5216 training documents respectively.
Wiki-40B: A new multilingual language model benchmark that is composed of 40+ languages spanning several scripts and linguistic families containing round 40 billion characters and aimed to accelerate the research of multilingual modeling.
LAMA: LAnguage Model Analysis (LAMA) consists of a set of knowledge sources, each comprised of a set of facts. LAMA is a probe for analyzing the factual and commonsense knowledge contained in pretrained language models.
WIT: Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.

Options:- A: MLDoc  B: Wiki-40B  C: LAMA  D: WIT
MLDoc