NLPExplorer

SemEval - 2022

Total Papers:- 234

Total Papers accross all years:- 1352

Total Citations :- 0

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Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Infrrd.ai at SemEval-2022 Task 11: A system for named entity recognition using data augmentation, transformer-based sequence labeling model, and EnsembleCRF

UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes

Arianna Muti | Katerina Korre | Alberto Barrón-Cedeño |

UMUTeam at SemEval-2022 Task 5: Combining image and textual embeddings for multi-modal automatic misogyny identification

José García-Díaz | Camilo Caparros-Laiz | Rafael Valencia-García |

Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework

Ahmed Mahran | Carlo Alessandro Borella | Konstantinos Perifanos |

PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection

JRLV at SemEval-2022 Task 5: The Importance of Visual Elements for Misogyny Identification in Memes

Jason Ravagli | Lorenzo Vaiani |

Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection

OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection

Lis Pereira | Ichiro Kobayashi |

Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection

Alejandro Mosquera |

YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic

Guangmin Zheng | Jin Wang | Xuejie Zhang |

SarcasmDet at SemEval-2022 Task 6: Detecting Sarcasm using Pre-trained Transformers in English and Arabic Languages

KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition

Caleb Martin | Huichen Yang | William Hsu |

LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover’s Distance