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 improve answer selection on [DATASET].

Keyphrases: answer selection

Dataset description:
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.
SuperGLUE: SuperGLUE is a benchmark dataset designed to pose a more rigorous test of language understanding than GLUE. SuperGLUE has the same high-level motivation as GLUE: to provide a simple, hard-to-game measure of progress toward general-purpose language understanding technologies for English.
NarrativeQA: The NarrativeQA dataset includes a list of documents with Wikipedia summaries, links to full stories, and questions and answers.
GYAFC: Grammarly’s Yahoo Answers Formality Corpus (GYAFC) is the largest dataset for any style containing a total of 110K informal / formal sentence pairs. Yahoo Answers is a question answering forum, contains a large number of informal sentences and allows redistribution of data.
CommonsenseQA: The CommonsenseQA is a dataset for commonsense question answering task. The dataset consists of 12,247 questions with 5 choices each.

Options:- A: SQuAD  B: SuperGLUE  C: NarrativeQA  D: GYAFC  E: CommonsenseQA
["SQuAD", "NarrativeQA"]