1. Akula, R., & Garibay, I. (2021a). Explainable detection of sarcasm in social media. In O. D. Clercq, A. Balahur, J. Sedoc, V. Barrière, S. Tafreshi, S. Buechel, & V. Hoste. (Eds.), Proceedings of the eleventh workshop on computational approaches to subjectivity, sentiment and social media analysis, WASSA@EACL, April 19 (pp. 34–39). Association for Computational Linguistics. https://aclanthology.org/2021.wassa-1.4/
2. Akula, R., & Garibay, I. (2021b). Interpretable multi-head self-attention architecture for sarcasm detection in social media. Entropy, 23(4), 394. https://doi.org/10.3390/e23040394
3. Amir, S., Wallace, B. C., Lyu, H., Carvalho, P., & Silva, M. J. (2016). Modelling context with user embeddings for sarcasm detection in social media. In Y. Goldberg, & S. Riezler. (Eds.), Proceedings of the 20th SIGNLL conference on computational natural language learning, CoNLL 2016, Berlin, Germany, August 11–12 (pp. 167–177). ACL. https://doi.org/10.18653/v1/k16-1017
4. Avvaru, A., Vobilisetty, S., & Mamidi, R. (2020). Detecting sarcasm in onversation context using transformer-based models. In B. B. Klebanov, E. Shutova, P. Lichtenstein, S. Muresan, C. W. Leong, A. Feldman, & D. Ghosh (Eds.), Proceedings of the second workshop on figurative language processing, Fig-Lang@ACL, July 9 (pp. 98–103). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.figlang-1.15
5. Bali, T., & Singh, N. (2016). Sarcasm detection: Building a contextual hierarchy. In M. Nissim, V. Patti, & B. Plank. (Eds.), Proceedings of the workshop on computational modeling of people’s opinions, personality, and emotions in social media, PEOPLES@COLING 2016, Osaka, Japan, December 12 (pp. 119–127). The COLING 2016 Organizing Committee. https://aclanthology.org/W16-4313/