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 propose a simple and effective method for sentence embedding that is based on knowledge distillation from cross-att

Keyphrases: sentence similarity text

Dataset description:
SBU Captions Dataset: A collection that allows researchers to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results.
SimpleQuestions: SimpleQuestions is a large-scale factoid question answering dataset. It consists of 108,442 natural language questions, each paired with a corresponding fact from Freebase knowledge base.
WebChild: One of the largest commonsense knowledge bases available, describing over 2 million disambiguated concepts and activities, connected by over 18 million assertions.
MRPC: Microsoft Research Paraphrase Corpus (MRPC) is a corpus consists of 5,801 sentence pairs collected from newswire articles. Each pair is labelled if it is a paraphrase or not by human annotators.
GenericsKB: The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge.
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.

Options:- A: SBU Captions Dataset  B: SimpleQuestions  C: WebChild  D: MRPC  E: GenericsKB  F: GLUE
["MRPC", "GLUE"]