Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors (2022.acl-long)
Copied to clipboard
| Challenge: | a new study shows that general abusive language classifiers are reliable in detecting explicit abuse but fail to detect more subtle abuses. |
| Approach: | They propose an interpretability technique to quantify the sensitivity of a trained model to new data . they propose a degree of explicitness metric to suggest out-of-domain unlabeled examples . |
| Outcome: | The proposed interpretability technique is useful for predicting the generalizability of the model on new data. |
Similar Papers
Implicitly Abusive Language – What does it actually look like and why are we not getting there? (2021.naacl-main)
Copied to clipboard
| 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 . |
| Outcome: | The proposed model could be improved to detect implicit abuse in a dataset with a standardized model. |
I Feel Offended, Don’t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language (2020.lrec-1)
Copied to clipboard
| Challenge: | Recent literature suggests different approaches to identify abusive language phenomena . however, there is a lack of data sets that take into account the degree of explicitness . |
| Approach: | They propose to use annotation guidelines to distinguish between explicit and implicit abuse in English and apply them to OLID/OffensEval. |
| Outcome: | The proposed tool distinguishes between explicit and implicit abuse in English and takes into account the degree of explicitness. |
Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language (2023.emnlp-main)
Copied to clipboard
| Challenge: | Currently, only explicit abuse can be reliably detected due to the increasing amount of abusive language on the Web. |
| Approach: | They propose a crowdsourced dataset that can detect euphemistic abuse by paraphrasing simple explicit utterances. |
| Outcome: | The proposed classifier augments training data with automatically-generated GPT-3 completions. |
Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for abusive language detection and content moderation are limited by regulatory bodies and social media platforms. |
| Approach: | They propose to replace existing datasets in English with synthetic data by rewriting original texts with an instruction-based generative model. |
| Outcome: | The proposed model improves performance in cross-dataset training. |
Revisiting Implicitly Abusive Language Detection: Evaluating LLMs in Zero-Shot and Few-Shot Settings (2025.coling-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed models outperform the best previously reported methods in classifying sentences directly as IAL or benign and extracting linguistic features associated with IAL. |
Detecting context abusiveness using hierarchical deep learning (D19-50)
Copied to clipboard
| Challenge: | Abusive text is a serious problem in social media and causes many issues among users . a model that detects text abusiveness in context without explicit abusive words is challenging . |
| Approach: | They propose to use an abusive lexicon to determine the existence of an abusive word in text . they combine local and global features to evaluate the model using benchmark data . |
| Outcome: | The proposed model outperforms all previous models for detecting abusiveness in text without abusive words. |
How to Solve Few-Shot Abusive Content Detection Using the Data We Actually Have (2024.lrec-main)
Copied to clipboard
| 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. |
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (P19-1)
Copied to clipboard
| Challenge: | Current methods to detect online abuse focus on a narrow definition of abuse to detriment of victims seeking validation and solutions. |
| Approach: | They argue that the NLP community needs to make three substantive changes to tackle both more subtle and more serious forms of abuse. |
| Outcome: | The proposed approach would address the problem of abuse in a more inclusive and productive way. |
Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition (D19-50)
Copied to clipboard
| Challenge: | Existing methods to detect abusive language only train one classifier for the whole variety of offending . a new method can support a moderator with explicit unraveled explanations for why something was flagged as abusive . |
| Approach: | a new method is proposed to distinguish explicitly abusive cases from the more "shadowed" ones . the researchers extend a lexicon of abusive terms to include new obfuscations of abusive words . |
| Outcome: | a new method can distinguish explicitly abusive cases from the more "shadowed" ones . the method can support a moderator with explicit unraveled explanations for why something was flagged as abusive . |
Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis (2021.eacl-main)
Copied to clipboard
| Challenge: | Using crowdsourcing, we can detect implicitly abusive comparisons . Abusive language is defined as hurtful, derogatory or obscene utterances made by one person to another . |
| Approach: | They propose to use crowdsourcing to generate a dataset for detecting implicitly abusive comparisons . they also use a range of linguistic features to better understand abusive comparison mechanisms . |
| Outcome: | The proposed dataset includes measures to obtain representative and unbiased comparisons. |