System Demo: Tool and Infrastructure for Offensive Language Error Analysis (OLEA) in English
This work addresses the need for better error analysis tools in offensive language detection, which is crucial for improving system performance in real-world applications, but it is incremental as it builds on existing methods.
The paper tackles the problem of error analysis in offensive language detection, where systems often fail on complex or implicit cases, by introducing OLEA, an open-source Python library that provides tools and infrastructure for analyzing errors and redistributing datasets and methods with minimal coding.
The automatic detection of offensive language is a pressing societal need. Many systems perform well on explicit offensive language but struggle to detect more complex, nuanced, or implicit cases of offensive and hateful language. OLEA is an open-source Python library that provides easy-to-use tools for error analysis in the context of detecting offensive language in English. OLEA also provides an infrastructure for re-distribution of new datasets and analysis methods requiring very little coding.