System Demo: Tool and Infrastructure for Offensive Language Error Analysis (OLEA) in English

TL;DR

OLEA is an open-source Python library designed to facilitate error analysis and dataset management for offensive language detection in English, addressing challenges in identifying nuanced and implicit offensive content.

Contribution

The paper introduces OLEA, a novel tool and infrastructure that simplifies error analysis and dataset handling for offensive language detection systems.

Findings

Enables detailed error analysis of offensive language detection models

Supports easy dataset re-distribution and analysis methods

Facilitates research on nuanced and implicit offensive language detection

Abstract

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.