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Found 14 papers, 6 papers with code
Six Attributes of Unhealthy Conversations
We present a new dataset of approximately 44000 comments labeled by crowdworkers.
SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI), which explores the detection of misogynous memes on the web by taking advantage of available texts and images.
From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer
We study the task of toxic spans detection, which concerns the detection of the spans that make a text toxic, when detecting such spans is possible.
Lost in Distillation: A Case Study in Toxicity Modeling
In an era of increasingly large pre-trained language models, knowledge distillation is a powerful tool for transferring information from a large model to a smaller one.
A New Generation of Perspective API: Efficient Multilingual Character-level Transformers
As such, it is crucial to develop models that are effective across a diverse range of languages, usages, and styles.
Toxicity Detection can be Sensitive to the Conversational Context
User posts whose perceived toxicity depends on the conversational context are rare in current toxicity detection datasets.
SemEval-2021 Task 5: Toxic Spans Detection
For the supervised sequence labeling approach and evaluation purposes, posts previously labeled as toxic were crowd-annotated for toxic spans.
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers
Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts.
Six Attributes of Unhealthy Conversation
We present a new dataset of approximately 44000 comments labeled by crowdworkers.
Toxicity Detection: Does Context Really Matter?
Moderation is crucial to promoting healthy on-line discussions.
Classifying Constructive Comments
The quality of the annotation scheme and the resulting dataset is evaluated using measurements of inter-annotator agreement, expert assessment of a sample, and by the constructiveness sub-characteristics, which we show provide a proxy for the general constructiveness concept.
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in society at large.
Limitations of Pinned AUC for Measuring Unintended Bias
This report examines the Pinned AUC metric introduced and highlights some of its limitations.
