Summary of Towards Generalized Offensive Language Identification, by Alphaeus Dmonte et al.
Towards Generalized Offensive Language Identification
by Alphaeus Dmonte, Tejas Arya, Tharindu Ranasinghe, Marcos Zampieri
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents an issue of prevalent online hate speech and cyberbullying, prompting machine learning (ML) and natural language processing (NLP) communities to develop automatic identification and mitigation systems. These systems use publicly available models or annotated datasets to detect potentially harmful content, but their generalizability is unclear. The paper empirically evaluates the generalizability of offensive language detection models and datasets across a novel benchmark, answering three research questions on this topic. The findings aim to create robust real-world offensive language detection systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem – people are mean to each other online! To stop this, scientists have made special computers that can find when someone is being mean or cruel online. These computers use different methods to work out if something is nice or nasty. The question is: do these computers really know how to spot mean things in all kinds of situations? This paper looks at how well these computers do in a special test, and what they found will help make the internet a safer place. |
Keywords
» Artificial intelligence » Machine learning » Natural language processing » Nlp » Prompting
