Search Results
Deep Learning Models for Multilingual Hate Speech Detection
Hate speech detection is a challenging problem with most of the datasets available in only one language: English.
Automated Hate Speech Detection and the Problem of Offensive Language
We train a multi-class classifier to distinguish between these different categories.
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media comments
The increased expansion of abusive content on social media platforms negatively affects online users.
Filipino Benchmarks for Measuring Sexist and Homophobic Bias in Multilingual Language Models from Southeast Asia
We expand on this line of research by introducing Filipino CrowS-Pairs and Filipino WinoQueer: benchmarks that assess both sexist and anti-queer biases in pretrained language models (PLMs) handling texts in Filipino, a low-resource language from the Philippines.
A Novel Interpretability Metric for Explaining Bias in Language Models: Applications on Multilingual Models from Southeast Asia
Work on bias in pretrained language models (PLMs) focuses on bias evaluation and mitigation and fails to tackle the question of bias attribution and explainability.
UMUTeam@LT-EDI-ACL2022: Detecting homophobic and transphobic comments in Tamil
This working-notes are about the participation of the UMUTeam in a LT-EDI shared task concerning the identification of homophobic and transphobic comments in YouTube.
Bridging the gap in online hate speech detection: a comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter
Our study addresses a significant gap in online hate speech detection research by focusing on homophobia, an area often neglected in sentiment analysis research.
