You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: We develop cross-lingual language models and demonstrate their effectiveness on cross

Keyphrases: cross-lingual natural language understanding

Dataset description:
XNLI: The Cross-lingual Natural Language Inference (XNLI) corpus is the extension of the Multi-Genre NLI (MultiNLI) corpus to 15 languages. The dataset was created by manually translating the validation and test sets of MultiNLI into each of those 15 languages.
WikiAnn: WikiAnn is a dataset for cross-lingual name tagging and linking based on Wikipedia articles in 295 languages.
XQA: XQA is a data which consists of a total amount of 90k question-answer pairs in nine languages for cross-lingual open-domain question answering.
EXAMS: A new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. Collects more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.
GLUE: General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding tasks, including single-sentence tasks CoLA and SST-2, similarity and paraphrasing tasks MRPC, STS-B and QQP, and natural language inference tasks MNLI, QNLI, RTE and WNLI.
XQuAD: XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi.

Options:- A: XNLI  B: WikiAnn  C: XQA  D: EXAMS  E: GLUE  F: XQuAD
["XNLI", "GLUE"]