Say ‘YES’ to Positivity: Detecting Toxic Language in Workplace Communications (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Toxic workplace communication is subtle, hidden or shows human biases . lack of corpus, sparsity of toxicity in enterprise emails hinder study . |
| Approach: | They propose a taxonomy to study toxic language at the workplace and a dataset to study it. |
| Outcome: | The proposed taxonomy provides a general and computationally viable taxonomies for studying toxic language at the workplace and analyzes why offensive language and hate-speech datasets are not suitable to detect workplace toxicity. |
Similar Papers
Challenges in Automated Debiasing for Toxic Language Detection (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for debiasing toxic language data are limited in their ability to prevent biased behavior in toxic language detection systems. |
| Approach: | They propose to debiase toxic language detection models using lexical and dialectal markers using synthetic labels instead of traditional methods. |
| Outcome: | The proposed method reduces dialectal associations with toxicity despite the use of synthetic labels . |
♪ Something Just Like TRuST ♪ *: Toxicity Recognition of Span and Target (2026.findings-acl)
Copied to clipboard
| Challenge: | Toxic language is pervasive online, and because LLMs are trained on web data, it generates such content. |
| Approach: | They propose a large-scale dataset that synthesizes toxicity definitions and an annotation scheme . they use a rigorous human annotation process to evaluate the diversity of the annotations . |
| Outcome: | The proposed model outperforms existing models on three tasks and is not reliable. |
Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis (2020.aacl-main)
Copied to clipboard
| Challenge: | Hate speech and toxic comments are a common concern of social media platform users . identifying toxic comments is important for studying and preventing the proliferation of toxicity in social media. |
| Approach: | They propose to use Brazilian Portuguese to analyze toxic or non-toxic tweets . they propose to analyze tweets as toxic or in different types of toxicity . |
| Outcome: | The proposed model achieves 76% macro-F1 score using monolingual data in the binary case. |
On the Role of Speech Data in Reducing Toxicity Detection Bias (2025.naacl-long)
Copied to clipboard
Samuel Bell, Mariano Coria Meglioli, Megan Richards, Eduardo Sánchez, Christophe Ropers, Skyler Wang, Adina Williams, Levent Sagun, Marta R. Costa-jussà
| Challenge: | Text toxicity detection systems produce disproportionate rates of false positives on demographic groups . toxicity classification systems often misinterpret benign group mentions as toxic . |
| Approach: | They use group annotations to compare text-based and speech-based toxicity detection systems. |
| Outcome: | The results show that access to speech data supports reduced bias against group mentions . the authors recommend improving classifiers, rather than transcription pipelines if possible . |
Humans Need Context, What about Machines? Investigating Conversational Context in Abusive Language Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | In this paper, we examine the role of conversational context in abusive language detection . prior studies have ignored the contextual nature of abusive language, ignoring this aspect . toxicity, hate speech, harmful stereotypes are among the forms of harmful language . |
| Approach: | They propose to use conversational context to analyze abusive language detection using two methods . they use "abusive language" as an umbrella term to refer to various forms of harmful language . |
| Outcome: | The proposed approach is based on two datasets in English and a new dataset of French tweets annotated for hate speech and stereotypes. |
A Stacking-based Efficient Method for Toxic Language Detection on Live Streaming Chat (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Existing methods for toxic language detection are based on deep learning, but they are not scalable considering inference speed and computational resources. |
| Approach: | They propose a method for toxic language detection that is aware of real-world scenarios by partial stacking partial stacks that feeds initial results with low confidence to meta-classifier. |
| Outcome: | The proposed method achieves faster inference speed than BERT-based models with comparable performance. |
No offence, Bert - I insult only humans! Multilingual sentence-level attack on toxicity detection networks (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a new sentence-level attack on toxic detection models is shown to work on seven languages . toxicity detection systems are used to silence the voices of criticism, causing echo chambers . |
| Approach: | They propose a sentence-level attack that adds positive words to a hateful message . they show the attack works on seven languages from three different language families . |
| Outcome: | The proposed attack is shown to work on seven languages from three different language families. |
Probing Toxic Content in Large Pre-Trained Language Models (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies on pre-trained language models have shown that they carry harmful biases towards different social groups. |
| Approach: | They propose a method to probe English, French, and Arabic PTLMs and quantify the potentially harmful content they convey with respect to a set of templates. |
| Outcome: | The proposed method analyzes PTLMs to predict masked tokens at the end of sentences to assess their toxicity. |
An Exploratory Analysis of the Relation between Offensive Language and Mental Health (2021.findings-acl)
Copied to clipboard
| Challenge: | Using computational models, the use of offensive language is pervasive in social media . a popular line of research is the study of machine learning classifiers to identify offensive content online . |
| Approach: | They analyze social media posts written by individuals with depression and those without . they train computational models to compare use of offensive language with depression detection . |
| Outcome: | The proposed models show that offensive language is more frequently used in the samples written by individuals with depression and those showing signs of depression. |
Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to address toxicity issues with large language models are inadequate . lack of domain-specific knowledge leads to false negatives and excessive sensitivity to toxic speech limits freedom of speech. |
| Approach: | They propose a method that leverages graph search on a meta-toxic knowledge graph to enhance hatred and toxicity detection. |
| Outcome: | The proposed method lowers false positive rate and improves toxicity detection performance in out-of-domain scenarios. |