SemEval 2022 Task
SemEval 2024 encompassed a series of shared tasks focused on advancing natural language processing (NLP), particularly in challenging areas like commonsense reasoning, biomedical text understanding, and machine-generated text detection. Research heavily utilized large language models (LLMs) such as BERT, RoBERTa, and various others, often incorporating techniques like chain-of-thought prompting, data augmentation, and in-context learning to improve performance on diverse tasks. These advancements contribute to a broader understanding of LLM capabilities and limitations, with implications for applications ranging from clinical decision support to combating misinformation.
Papers
UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification
Andrei Paraschiv, Mihai Dascalu, Dumitru-Clementin Cercel
SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models
Changyu Hou, Jun Wang, Yixuan Qiao, Peng Jiang, Peng Gao, Guotong Xie, Qizhi Lin, Xiaopeng Wang, Xiandi Jiang, Benqi Wang, Qifeng Xiao
UAlberta at SemEval 2022 Task 2: Leveraging Glosses and Translations for Multilingual Idiomaticity Detection
Bradley Hauer, Seeratpal Jaura, Talgat Omarov, Grzegorz Kondrak
Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings
Timothee Mickus, Kees van Deemter, Mathieu Constant, Denis Paperno
HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models
Minghuan Tan