How to Solve Few-Shot Abusive Content Detection Using the Data We Actually Have (2024.lrec-main)
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| Challenge: | Existing datasets for abusive language detection are expensive and lack of knowledge about the target is a challenge. |
| Approach: | They propose to build models cheaply for a new target label set and/or language, using only a few training examples of the target domain. |
| Outcome: | The proposed model improves monolingually and across languages using existing datasets and only a few-shots of the target domain. |
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| Challenge: | Detecting online abusive language in social media messages is gaining increasing attention from scholars and stakeholders. |
| Approach: | They propose a hybrid approach with deep learning and a multilingual lexicon to cross-domain and cross-lingual detection of abusive content. |
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Revisiting Implicitly Abusive Language Detection: Evaluating LLMs in Zero-Shot and Few-Shot Settings (2025.coling-main)
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| Challenge: | Current research focuses on explicit abusive language, but subtler forms of IAL remain insufficiently studied. |
| Approach: | They evaluate the models' capabilities in classifying sentences directly as either IAL or benign, and in extracting linguistic features associated with IAL. |
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Inducing a Lexicon of Abusive Words – a Feature-Based Approach (N18-1)
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| Challenge: | a new classification task is needed to identify abusive words among a set of negative polar expressions. |
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Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data (2024.findings-acl)
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| Challenge: | Existing datasets for abusive language detection and content moderation are limited by regulatory bodies and social media platforms. |
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| Challenge: | Online abusive content detection, particularly in low-resource settings, remains underexplored. |
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XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages (2020.coling-main)
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| Challenge: | XHate-999 is a multi-domain and multilingual evaluation data set for abusive language detection . we show that domain- and language-adaption can lead to substantially improved abusive language detecting in the target language . |
| Approach: | They propose a multi-domain and multilingual evaluation data set for abusive language detection that allows for disentanglement of domain transfer and language transfer effects. |
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Reducing Gender Bias in Abusive Language Detection (D18-1)
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| Challenge: | Abusive language detection models tend to be biased toward identity words of a certain group of people . recent studies have raised concerns about the robustness of such systems . |
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No offence, Bert - I insult only humans! Multilingual sentence-level attack on toxicity detection networks (2023.findings-emnlp)
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| 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 . |
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Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection (2023.emnlp-main)
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| Challenge: | Existing methods for few-shot cross-lingual transfer learning are limited in target languages due to the scarcity of resources. |
| Approach: | They propose a method which interpolates pairs of instances based on the angle of their representations and propose augmentation methods to enhance few-shot cross-lingual abusive language detection. |
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Implicitly Abusive Language – What does it actually look like and why are we not getting there? (2021.naacl-main)
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| Challenge: | Existing datasets make learning implicit abuse difficult, argues a new position paper . a lack of work on implicit abuse has limited the effectiveness of automatic detection . |
| Approach: | They argue that existing datasets make learning implicit abuse difficult . they propose a divide-and-conquer strategy to detect implicit abuse . |
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