arXiv:2608. 15619v1 Announce Type: new Abstract: Emotion recognition from text keeps improving on benchmarks, yet whether an accuracy ceiling has been reached is seldom asked with discipline.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 28772v1 Announce Type: cross Abstract: Hate speech annotation pipelines routinely collapse annotator disagreement into majority vote labels before training.
arXiv:2608. 15338v1 Announce Type: cross Abstract: Sentiment classifiers are increasingly applied to social media content that is either sarcastic or AI-generated --- two distributional regimes where standard evaluations offer little guidance.
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:2606. 27536v1 Announce Type: cross Abstract: Speech emotion recognition (SER) often relies on hard consensus labels that collapse annotator disagreement.
arXiv:2604. 07801v2 Announce Type: replace-cross Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language.
arXiv:2606. 05376v1 Announce Type: new Abstract: Many human-centered tasks, including natural language inference (NLI) and emotion recognition (ER), have multiple plausible interpretations, leading to label ambiguity and challenging disagreements across human annotators.