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: This paper proposes a new learning criterion for reading comprehension, which is a combination of the maximum likelihood estimation

Keyphrases: reading comprehension text

Dataset description:
Torque: Torque is an English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships.
SQuAD: The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text.
VisualMRC: VisualMRC is a visual machine reading comprehension dataset that proposes a task: given a question and a document image, a model produces an abstractive answer. You can find more details, analyses, and baseline results in the paper,  VisualMRC: Machine Reading Comprehension on Document Images, AAAI 2021.
CoQA: CoQA is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation.
ReCoRD: Reading Comprehension with Commonsense Reasoning Dataset (ReCoRD) is a large-scale reading comprehension dataset which requires commonsense reasoning. ReCoRD consists of queries automatically generated from CNN/Daily Mail news articles; the answer to each query is a text span from a summarizing passage of the corresponding news.

Options:- A: Torque  B: SQuAD  C: VisualMRC  D: CoQA  E: ReCoRD
["SQuAD", "CoQA"]