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Found 7 papers, 6 papers with code
What If Ground Truth Is Subjective? Personalized Deep Neural Hate Speech Detection
This supports our overall observation that personalized models should always be considered in all subjective NLP tasks, including hate speech detection.
Towards Model-Based Data Acquisition for Subjective Multi-Task NLP Problems
Data annotated by humans is a source of knowledge by describing the peculiarities of the problem and therefore fueling the decision process of the trained model.
PALS: Personalized Active Learning for Subjective Tasks in NLP
In this paper, we present novel Personalized Active Learning techniques for Subjective NLP tasks (PALS) to either reduce the cost of the annotation process or to boost the learning effect.
ChatGPT: Jack of all trades, master of none
Our comparison of its results with available State-of-the-Art (SOTA) solutions showed that the average loss in quality of the ChatGPT model was about 25% for zero-shot and few-shot evaluation.
Assessment of Massively Multilingual Sentiment Classifiers
We use these to assess 11 models and 80 high-quality sentiment datasets (out of 342 raw datasets collected) in 27 languages and included results on the internally annotated datasets.
Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection
There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers.
Personal Bias in Prediction of Emotions Elicited by Textual Opinions
Analysis of emotions elicited by opinions, comments, or articles commonly exploits annotated corpora, in which the labels assigned to documents average the views of all annotators, or represent a majority decision.
