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: Probing experiments for analyzing word embeddings for 24 languages.

Keyphrases: probing text

Dataset description:
FrameNet: FrameNet is a linguistic knowledge graph containing information about lexical and predicate argument semantics of the English language. FrameNet contains two distinct entity classes: frames and lexical units, where a frame is a meaning and a lexical unit is a single meaning for a word.
WNLaMPro: The WordNet Language Model Probing (WNLaMPro) dataset consists of relations between keywords and words. It contains 4 different kinds of relations: Antonym, Hypernym, Cohyponym and Corruption.
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.
SNLI: The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and asked to generate entailing, contradicting, and neutral sentences.
MultiNLI: The Multi-Genre Natural Language Inference (MultiNLI) dataset has 433K sentence pairs. Its size and mode of collection are modeled closely like SNLI.
VideoMem: Composed of 10,000 videos annotated with memorability scores. In contrast to previous work on image memorability -- where memorability was measured a few minutes after memorization -- memory performance is measured twice: a few minutes after memorization and again 24-72 hours later.

Options:- A: FrameNet  B: WNLaMPro  C: XNLI  D: SNLI  E: MultiNLI  F: VideoMem
["XNLI", "SNLI", "MultiNLI"]