arXiv Computation and Language
Labels have Human Values: Value Calibration of Subjective Tasks
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv Machine Learning
Jun 9
Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning
arXiv:2009. 10277v2 Announce Type: replace-cross Abstract: We propose a system for measuring hate speech on a continuous, interval-valued spectrum ranging from genocidal to supportive speech by combining supervised deep learning with faceted Rasch item response theory (IRT).
arXiv Machine Learning
Aug 6
Learning Sexism Detection Using Multi-Agent Perspectivist Preference Optimization
arXiv:2608. 04056v1 Announce Type: cross Abstract: When people label text for sexism, they often disagree, and not because some of them are wrong: they genuinely perceive sexism differently.
arXiv AI
Sep 2
ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation
ValueGraph is a graph pre‑training framework that incorporates automatically inferred moral‑value signals as soft constraints to learn contextualized user representations. By constructing post‑reply graphs, it captures both semantic and structural information and aligns users through contrastive and clustering objectives based on relative value similarity. Experiments on stance detection and Twitter bot detection demonstrate consistent improvements over strong text‑based, graph‑based, and LLM baselines, underscoring the benefit of value‑signal guidance for socially informed user modeling.
Hugging Face Trending Papers
Jun 22
Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts
Understanding moral values in social media text offers insight into moral judgement formation, and supervised NLP models trained on crowdsourced data have achieved strong classification performance. However, most approaches simplify the problem by aggregating multiple annotators' labels into a single "ground truth", overlooking the inherent subjectivity of the task.
arXiv AI
Jun 9
Dealing with Annotator Disagreement in Hate Speech Classification
arXiv:2502. 08266v3 Announce Type: replace-cross Abstract: Hate speech detection is a crucial task, especially on social media where harmful content can spread quickly.
arXiv Machine Learning
Jul 10
Ensemble Diversity Optimization for Subjective Supervision
arXiv:2607. 08493v1 Announce Type: new Abstract: Subjective NLP tasks often exhibit systematic annotator disagreement, requiring models that represent uncertainty rather than collapse it.