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IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English

arXiv 2203.02244 ↗First posted 4 Mar 2022cs.CLcited by 0

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Abstract (arXiv)

This paper describes the system architectures and the models submitted by our team "IISERBBrains" to SemEval 2022 Task 6 competition. We contested for all three sub-tasks floated for the English dataset. On the leader-board, wegot19th rank out of43 teams for sub-taskA, the 8th rank out of22 teams for sub-task B,and13th rank out of 16 teams for sub-taskC. Apart from the submitted results and models, we also report the other models and results that we obtained through our experiments after organizers published the gold labels of their evaluation data

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 4 Mar 2022.

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References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  4. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  5. 2019Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)cited by 173
  6. 2018Reasoning with Sarcasm by Reading In-betweencited by 38
  7. 2018Automatic Detection of Cyberbullying in Social Media Textcited by 25
  8. 2020A Report on the 2020 Sarcasm Detection Shared Taskcited by 23
  9. 2018Sarcasm Analysis using Conversation Contextcited by 17
  10. 2020Code-switching patterns can be an effective route to improve performance of downstream NLP applications: A case study of humour, sarcasm and hate speech detectioncited by 12
  11. 2021Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference Taskcited by 8

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