Fine Grained Classification

Implicit discourse relation recognition remains a challenging task as state-of-the-art approaches reach F1 scores ranging from 9.95% to 37.67% on the 2016 CoNLL shared task. In our work, we explore …...

AbstractThe quality estimation of artifacts generated by creators via crowdsourcing has great significance for the construction of a large-scale data resource. A common approach to this problem is to ask …...

AbstractExisting text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases. To …...

AbstractFine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them. Guiding the model to focus on differentiating dimensions between these commonly confusable classes is …...

AbstractFor school teachers and Designated Safeguarding Leads (DSLs), computers and other school-owned communication devices are both indispensable and deeply worrisome. For their education, children require access to the Internet, as …...

AbstractThis paper describes SemEval-2022’s shared task “Explainable Detection of Online Sexism”. The fine-grained classification of sexist content plays a major role in building explainable frameworks for online sexism detection. We …...