Update the citation.

README.md

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 Given the scarcity of datasets for understanding natural language in visual scenes, we introduce a novel textual entailment dataset, named Textual Natural Contextual Classification (TNCC). 
 This dataset is formulated on the foundation of Crisscrossed Captions (https://github.com/google-research-datasets/Crisscrossed-Captions), an image captioning dataset supplied with human-rated semantic similarity ratings on a continuous scale from 0 to 5. 
 We tailor the dataset to suit a binary classification task. Specifically, sentence pairs with annotation scores exceeding 4 are categorized as positive (entailment), whereas pairs with scores less than 1 are marked as negative (non-entailment). 
-The TNCC dataset is partitioned into training, validation, and testing sets, containing 3,600, 1,200, and 1,560 instances, respectively.
+The TNCC dataset is partitioned into training, validation, and testing sets, containing 3,600, 1,200, and 1,560 instances, respectively.
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+If you use this dataset for academic research, please cite the NeurIPS 2023 paper titled 'Back-Modality: Leveraging Modal Transformation for Data Augmentation'.
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