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: I want to train a reading comprehension model. The model is used to answer questions about

Keyphrases: reading comprehension style question answering text paragraph-level

Dataset description:
DuoRC: DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie. Why another RC dataset?
TriviaQA: TriviaQA is a realistic text-based question answering dataset which includes 950K question-answer pairs from 662K documents collected from Wikipedia and the web. This dataset is more challenging than standard QA benchmark datasets such as Stanford Question Answering Dataset (SQuAD), as the answers for a question may not be directly obtained by span prediction and the context is very long.
MCScript: MCScript is used as the official dataset of SemEval2018 Task11. This dataset constructs a collection of text passages about daily life activities and a series of questions referring to each passage, and each question is equipped with two answer choices.
MOCHA: Contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation.
ComplexWebQuestions: ComplexWebQuestions is a dataset for answering complex questions that require reasoning over multiple web snippets. It contains a large set of complex questions in natural language, and can be used in multiple ways:   By interacting with a search engine; As a reading comprehension task: the authors release 12,725,989 web snippets that are relevant for the questions, and were collected during the development of their model; As a semantic parsing task: each question is paired with a SPARQL query that can be executed against Freebase to retrieve the answer.
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.
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.

Options:- A: DuoRC  B: TriviaQA  C: MCScript  D: MOCHA  E: ComplexWebQuestions  F: VisualMRC  G: SQuAD
["TriviaQA", "SQuAD"]