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 model for natural language inference (NLI) on a dataset

Keyphrases: natural language inference text

Dataset description:
e-SNLI: e-SNLI is used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets.
MultiNLI: The Multi-Genre Natural Language Inference (MultiNLI) dataset has 433K sentence pairs. Its size and mode of collection are modeled closely like SNLI.
FarsTail: Natural Language Inference (NLI), also called Textual Entailment, is an important task in NLP with the goal of determining the inference relationship between a premise p and a hypothesis h. It is a three-class problem, where each pair (p, h) is assigned to one of these classes: "ENTAILMENT" if the hypothesis can be inferred from the premise, "CONTRADICTION" if the hypothesis contradicts the premise, and "NEUTRAL" if none of the above holds. There are large datasets such as SNLI, MNLI, and SciTail for NLI in English, but there are few datasets for poor-data languages like Persian.
SNLI: The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and asked to generate entailing, contradicting, and neutral sentences.
HELP: The HELP dataset is an automatically created natural language inference (NLI) dataset that embodies the combination of lexical and logical inferences focusing on monotonicity (i.e., phrase replacement-based reasoning). The HELP (Ver.1.0) has 36K inference pairs consisting of upward monotone, downward monotone, non-monotone, conjunction, and disjunction.
NLI-TR: Natural Language Inference in Turkish (NLI-TR) provides translations of two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels.

Options:- A: e-SNLI  B: MultiNLI  C: FarsTail  D: SNLI  E: HELP  F: NLI-TR
["MultiNLI", "SNLI"]