Many interpretability tools allow practitioners and researchers to explain Natural Language Processing systems. However, each tool requires different configurations and provides explanations in different forms, hindering the possibility of assessing …
Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing supplies appropriate algorithms for trying to reach this objective, all research efforts are directed toward the …
Hate speech detection models are typically evaluated on held-out test sets. However, this risks painting an incomplete and potentially misleading picture of model performance because of increasingly well-documented systematic gaps and biases in hate …
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the …
Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted exclusively on English and very few consistently labeled, non-English resources have been proposed. Learning to …
Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that **4.3% of the time, language models complete a sentence with a hurtful …
The task of Named Entity Recognition (NER) is aimed at identifying named entities in a given text and classifying them into pre-defined domain entity types such as persons, organizations, locations. Most of the existing NER systems make use of …