← Back to all news
arXiv AI By Xinyi Vicky Xiang, Catherine Qingyun Wang, Paula Cort\'es Lemos

Patronizing and Condescending Language Detection in English and Mandarin

Read the original on arXiv AI →

The paper introduces a system for automatically detecting patronizing and condescending language (PCL) in English and Mandarin. Two approaches are explored: a convolutional neural network with static word embeddings, and a BERT-based model fine‑tuned for the PCL task. The BERT approach outperformed the other, achieving an F1 score of 0.528 on the development set.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Computation and Language
Aug 27

From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation

The paper presents an instruction‑tuned large language model (LLM) based on Qwen3 that is fine‑tuned for hate speech mitigation by unifying 36 English hate speech datasets. The authors show that this generalist LLM achieves state‑of‑the‑art performance on in‑domain benchmarks and delivers significant gains in cross‑domain and cross‑lingual generalization, outperforming specialist encoder‑based classifiers.

By Lukas Edman, Daryna Dementieva, Alexander Fraser
llmsnlpfine-tuningbenchmarks
More like this →
arXiv AI
Aug 28

Training-Time Explainability for Multilingual Hate Speech Detection: Aligning Model Reasoning with Human Rationales

The paper proposes a training‑time explainability framework that aligns model reasoning with human‑annotated rationales to improve both classification performance and interpretability for multilingual hate speech detection. It is evaluated on HateXplain (English) and BullySent (Hinglish), datasets that capture anti‑Muslim hate in culturally coded, multilingual forms. Using methods such as LIME, Integrated Gradients, Grad‑X‑Input, and attention, the study shows that gradient‑ and attention‑based regularization boosts F‑scores, enhances plausibility and faithfulness, and captures culturally specific cues for detecting implicit anti‑Muslim hate.

By Muhammad Deedahwar Mazhar Qureshi, Sannaan Khan, Muhammad Atif Qureshi, Wael Rashwan
safety
More like this →