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 test the interpretability of a sentence embedding model on movie dialogs and the [DATASET] dataset.

Keyphrases: interpretability text

Dataset description:
COCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
MDD: Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion).
TRIPOD: TRIPOD contains screenplays and plot synopses with turning point (TP) annotations for 99 movies. Each movie contains:   The Wikipedia plot synopsis (extended summary of 35 sentences on average) with sentence-level TP annotations.
CCPE-M: A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language. The corpus was constructed from dialogues between two paid crowd-workers using a Wizard-of-Oz methodology.
SentEval: SentEval is a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity.

Options:- A: COCO  B: MDD  C: TRIPOD  D: CCPE-M  E: SentEval
["COCO", "SentEval"]