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 am developing a reading comprehension model for the QuAC dataset.

Keyphrases: question answering text

Dataset description:
SberQuAD: A large scale analogue of Stanford SQuAD in the Russian language - is a valuable resource that has not been properly presented to the scientific community. See DeepPavlov link  Model results | Model config                 | EM (dev)    | F-1 (dev)   | |------------------------------|-------------|-------------| |DeepPavlov RuBERT            | 66.30+-0.24 | 84.60+-0.11 | | DeepPavlov multilingual BERT | 64.35+-0.39 | 83.39+-0.08 | | DeepPavlov R-Net             | 60.62       | 80.04       |
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.
DROP: Discrete Reasoning Over Paragraphs DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was necessary for prior datasets.
BiPaR: BiPaR is a manually annotated bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support monolingual, multilingual and cross-lingual reading comprehension on novels. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written in parallel in two languages.
NewsQA: The NewsQA dataset is a crowd-sourced machine reading comprehension dataset of 120,000 question-answer pairs. Documents are CNN news articles.
ConvAI2: The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset.

Options:- A: SberQuAD  B: SQuAD  C: DROP  D: BiPaR  E: NewsQA  F: ConvAI2
["SQuAD", "NewsQA"]