| Challenge: | a recent study shows that Brown clustering is of little use when distinguishing word polarity in sentiment analysis tasks. |
| Approach: | They investigate the use of Brown clustering for offensive language detection . they train Brown clusters separately on positive and negative sentiment data, then combine it into a single complex feature per word . |
| Outcome: | The proposed method improves offensive language detection when used as the only feature or with words or character n-grams. |
Similar Papers
Representing and Clustering Errors in Offensive Language Detection (2025.naacl-srw)
Copied to clipboard
| Challenge: | Sentence-BERT embeddings of Large Language Model (LLM)-generated linguistic features give the most interpretable clustering for Arabic errors. |
| Approach: | They evaluate the K-Means clustering of four text representations for the task of offensive language detection in English and Levantine Arabic. |
| Outcome: | The proposed clustering of four text representations for offensive language detection in English and Levantine Arabic gives the most human-interpretable clustering for English errors and the grouping is mainly based on the targeted group in the text. |
COLD: A Benchmark for Chinese Offensive Language Detection (2022.emnlp-main)
Copied to clipboard
| Challenge: | Offensive language detection is crucial for maintaining a civilized social media platform and deploying pre-trained language models. |
| Approach: | They propose a benchmark benchmark for Chinese offensive language analysis including a Chinese Offensive Language Dataset and a baseline detector which is trained on the dataset. |
| Outcome: | The proposed benchmark contributes to Chinese offensive language detection which is challenging for existing resources. |
“Why do I feel offended?” - Korean Dataset for Offensive Language Identification (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for detecting offensive content rely on labeled datasets, but few consider low-resource languages with relatively less data available for training. |
| Approach: | They propose to use Korean as a dataset for offensive language identification . they propose to perform abusive language detection and sentiment analysis to help identify offensive languages. |
| Outcome: | The proposed datasets improve the performance of offensive language identification in Korean, while the existing methods are limited. |
Offensive language detection in Hebrew: can other languages help? (2022.lrec-1)
Copied to clipboard
| Challenge: | Various approaches for offensive language detection have been applied for this task . contamination of social networks with offensive content is a new reality affecting almost all of us . |
| Approach: | They propose to use multiple supervised models and text representations to detect offensive language in three languages, including two Semitic languages. |
| Outcome: | The proposed model can detect offensive content in two Semitic languages, including Hebrew and Arabic, and it is able to perform cross-lingual and multilingual learning. |
Hate-Speech and Offensive Language Detection in Roman Urdu (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing research on hate-speech and offensive language detection in social media content is mainly focused on the English language. |
| Approach: | They propose to use an annotated dataset to detect hate-speech and offensive language in social media content . they propose to transfer five existing embedding models to Roman Urdu to test their performance . |
| Outcome: | The proposed model outperforms existing methods on RUHSOLD dataset and train domain-specific embeddings on more than 4.7 million tweets. |
Don’t be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks (2024.findings-naacl)
Copied to clipboard
| Challenge: | Offensive language detection is important for filtering out abusive expressions, authors argue . authors propose user-intended adversarial attacks that insert special symbols or leverage distinctive features of the Korean language. |
| Approach: | They propose user-intended adversarial attacks that insert special symbols or leverage the distinctive features of the Korean language. |
| Outcome: | The proposed models are more robust to performance degradation even when the attack rate is increased, compared to models trained on noisy texts. |
On the Robustness of Offensive Language Classifiers (2022.acl-long)
Copied to clipboard
| Challenge: | Existing studies on offensive language classifiers have focused on primitive attacks such as misspellings and extraneous spaces. |
| Approach: | They analyze the robustness of offensive language classifiers against crafty adversarial attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
| Outcome: | The proposed classifiers are robust against more crafty attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
Offensive Content Detection via Synthetic Code-Switched Text (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to detect offensive content in social media platforms are limited by the availability of labeled code-switched data. |
| Approach: | They propose a method for generating synthetic code-switched offensive content data using human-generated data and a keyword classification baseline. |
| Outcome: | The proposed algorithm can be used to generate synthetic code-switched offensive content data and train it on human-generated data. |
Multilingual Offensive Language Identification with Cross-lingual Embeddings (2020.emnlp-main)
Copied to clipboard
| Challenge: | Several studies investigating methods to detect offensive content in social media use English data. |
| Approach: | They apply cross-lingual contextual embeddings and transfer learning to make predictions in languages with less resources. |
| Outcome: | The proposed method compares favorably to the best systems submitted to recent shared tasks on Bengali, Hindi, and Spanish. |
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification (2021.findings-acl)
Copied to clipboard
| Challenge: | toxicity, hate speech, cyberbullying, and cyber-aggression are common themes in social media . authors present a dataset that is limited in size and biased towards offensive language . |
| Approach: | They present an expanded dataset that uses a taxonomy for offensive language identification . they show that using SOLID and OLID yields sizable performance gains . |
| Outcome: | The proposed dataset shows that it performs better than the OLID dataset for two different models. |