NLP

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 are presented.

This work proposes a novel approach to capture the uncertainty of the forecast using conditional Normalizing Flows, which allows us to model complex multimodal distributions and to compare various models using negative log-likelihood (NLL).

This paper introduces personalized deep learning models for subjective tasks in natural language processing (NLP), such as detecting hate speech, emotional text, and humor. These models consider individual opinions and beliefs, significantly outperforming generalized approaches across 14 NLP tasks. The study finds that personalized Human Bias (HuBi) methods greatly enhance reasoning quality, with multi-task solutions proving more effective than single-task ones, while human and word embeddings offer supplementary insights.

A new approach to the task of predicting humor in the text by applying the idea of a personalized approach that takes into account both the text and the context of the content receiver and shows that the user-related data reflecting an individual sense of humor has similar importance as the evaluated text itself.

A unified gold standard commonly exploited in natural language processing (NLP) tasks requires high inter-annotator agreement. However, …