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 present a BERT-based model that takes as input a multimodal input and is pretrained

Keyphrases: visual question answering images text

Dataset description:
SIMMC: Situated Interactive MultiModal Conversations (SIMMC) is the task of taking multimodal actions grounded in a co-evolving multimodal input content in addition to the dialog history. This dataset contains two SIMMC datasets totalling ~13K human-human dialogs (~169K utterances) using a multimodal Wizard-of-Oz (WoZ) setup, on two shopping domains: (a) furniture (grounded in a shared virtual environment) and (b) fashion (grounded in an evolving set of images).
Flickr30k: The Flickr30k dataset contains 31,000 images collected from Flickr, together with 5 reference sentences provided by human annotators.
VCR: Visual Commonsense Reasoning (VCR) is a large-scale dataset for cognition-level visual understanding. Given a challenging question about an image, machines need to present two sub-tasks: answer correctly and provide a rationale justifying its answer.
DAQUAR: DAQUAR (DAtaset for QUestion Answering on Real-world images) is a dataset of human question answer pairs about images.
QNLI: The QNLI (Question-answering NLI) dataset is a Natural Language Inference dataset automatically derived from the Stanford Question Answering Dataset v1.1 (SQuAD). SQuAD v1.1 consists of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator).

Options:- A: SIMMC  B: Flickr30k  C: VCR  D: DAQUAR  E: QNLI
["Flickr30k", "VCR"]