🔗 Who cites «NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs»

20 papers of our library cite it (of the 141,840 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs»

BERTweet: A pre-trained language model for English TweetsConference on Empirical Methods in Natural Language Processing (EMNLP)Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen2020 · 1,142 citations
Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion ModelAnnual Meeting of the Association for Computational Linguistics (ACL)Yitao Cai, Huiyu Cai, Xiaojun Wan2019 · 450 citations
A Transformer-based approach to Irony and Sarcasm detectionNeural Computing and ApplicationsRolandos Alexandros Potamias, Georgios Siolas, Andreas - Georgios Stafylopatis2019 · 312 citations
Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic AssociationAnnual Meeting of the Association for Computational Linguistics (ACL)Nan Xu, Zhixiong Zeng, Wenji Mao2020 · 206 citations
What A Sunny Day ☔: Toward Emoji-Sensitive Irony DetectionWorkshop on Noisy User-generated TextShirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, et al.2019 · 10 citations
1. Computational Sarcasm Analysis on Social Media: A Systematic ReviewarXiv:2209.06170Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, et al.2022 · 4 citations
   Tackling Irony Detection using Ensemble ClassifiersLanguage Resources and Evaluation Conference (LREC)Christoph Turban, Udo Kruschwitz2022 · 1 citation

13 more papers of our library cite it; they have no page here yet.

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