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Found 7 papers, 4 papers with code
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators’ Disagreement
Since state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media.
DH-FBK at SemEval-2022 Task 4: Leveraging Annotators’ Disagreement and Multiple Data Views for Patronizing Language Detection
The subtle and typically unconscious use of patronizing and condescending language (PCL) in large-audience media outlets undesirably feeds stereotypes and strengthens power-knowledge relationships, perpetuating discrimination towards vulnerable communities.
Ranked #3 on
Binary Condescension Detection
on DPM
Binary Condescension Detection
Multi-label Condescension Detection
Work Hard, Play Hard: Collecting Acceptability Annotations through a 3D Game
In this work, we contribute to this debate by presenting a 3D video game that was used to collect acceptability judgments on Italian sentences.
The Geography of Information Diffusion in Online Discourse on Europe and Migration
Language (English) is pivotal for information to become transnational and reach far.
SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)
We report on the second LeWiDi shared task, which differs from the first edition in three crucial respects: (i) it focuses entirely on NLP, instead of both NLP and computer vision tasks in its first edition; (ii) it focuses on subjective tasks, instead of covering different types of disagreements-as training with aggregated labels for subjective NLP tasks is a particularly obvious misrepresentation of the data; and (iii) for the evaluation, we concentrate on soft approaches to evaluation.
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement
Since state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media.
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus
The development of automated approaches to linguistic acceptability has been greatly fostered by the availability of the English CoLA corpus, which has also been included in the widely used GLUE benchmark.
