Update README.md

README.md

@@ -47,15 +47,10 @@ The dataset may contain biases inherent in the selection and annotation process,
 Users are advised to consider the limitations of the dataset when training and evaluating sarcasm detection models.
 
 ## Citation [optional]
+Khodak, M., Saunshi, N., & Vodrahalli, K. (2018). A Large Self-Annotated Corpus for Sarcasm. In LREC 2018 (pp. 1-6).
+Rahman M O, Hossain M S, Junaid T S, et al. Predicting prices of stock market using gated recurrent units (GRUs) neural networks[J]. Int. J. Comput. Sci. Netw. Secur, 2019, 19(1): 213-222.
+Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural computation, 2019, 31(7): 1235-1270.
+Gole, M., Nwadiugwu, W. P., & Miranskyy, A. (2023). On Sarcasm Detection with OpenAI GPT-based Models.
+B. Sonare, J. H. Dewan, S. D. Thepade, V. Dadape, T. Gadge and A. Gavali, "Detecting Sarcasm in Reddit Comments: A Comparative Analysis," 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India, 2023, pp. 1-6, doi: 10.1109/INCET57972.2023.10170613.
 
-**BibTeX:**
 
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-**APA:**
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-## Dataset Card Contact
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-[Contact information for inquiries or issues.]