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: The model consists of a retrieve-and-edit architecture for pun generation.

Keyphrases: pun generation text

Dataset description:
eSCAPE: Consists of millions of entries in which the MT element of the training triplets has been obtained by translating the source side of publicly-available parallel corpora, and using the target side as an artificial human post-edit. Translations are obtained both with phrase-based and neural models.
WikiText-103: The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.
BookCorpus: BookCorpus is a large collection of free novel books written by unpublished authors, which contains 11,038 books (around 74M sentences and 1G words) of 16 different sub-genres (e.g., Romance, Historical, Adventure, etc. ).
NAS-Bench-101: NAS-Bench-101 is the first public architecture dataset for NAS research. To build NASBench-101, the authors carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional architectures.
Retrieval-SfM: The Retrieval-SFM dataset is used for instance image retrieval. The dataset contains 28559 images from 713 locations in the world.

Options:- A: eSCAPE  B: WikiText-103  C: BookCorpus  D: NAS-Bench-101  E: Retrieval-SfM
["WikiText-103", "BookCorpus"]