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Found 11 papers, 5 papers with code
GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection?
A recent proposal in this direction is HateCheck, a suite for testing fine-grained model functionalities on synthesized data generated using templates of the kind "You are just a [slur] to me."
Towards Weakly-Supervised Hate Speech Classification Across Datasets
As pointed out by several scholars, current research on hate speech (HS) recognition is characterized by unsystematic data creation strategies and diverging annotation schemata.
Plot Writing From Pre-Trained Language Models
We propose generating story plots using off-the-shelf PLMs while maintaining the benefit of content planning to generate cohesive and contentful stories.
Seed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation
We then train interim models with individual candidate seed words.
Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia
The widely used advertising content taxonomy (IAB taxonomy) consists of 23 coarse-grained categories and 355 fine-grained categories.
Learning Only from Relevant Keywords and Unlabeled Documents
We consider a document classification problem where document labels are absent but only relevant keywords of a target class and unlabeled documents are given.
Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising
In this paper we propose a lightly-supervised framework to rapidly build text classifiers for contextual advertising.
