GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection

TL;DR

This paper presents domain-tuned BERT models for offensive language detection in English, achieving high F1 scores across three sub-tasks and demonstrating the effectiveness of domain adaptation and model stacking.

Contribution

The paper introduces domain-tuned BERT models with stacked components for offensive language detection, showing significant performance improvements over baseline models.

Findings

Domain tuning improves classification performance.

Stacked models enhance sub-task accuracy.

Achieved high F1 scores: 91.7%, 66.5%, 63.2%.

Abstract

Offensive language detection is an important and challenging task in natural language processing. We present our submissions to the OffensEval 2020 shared task, which includes three English sub-tasks: identifying the presence of offensive language (Sub-task A), identifying the presence of target in offensive language (Sub-task B), and identifying the categories of the target (Sub-task C). Our experiments explore using a domain-tuned contextualized language model (namely, BERT) for this task. We also experiment with different components and configurations (e.g., a multi-view SVM) stacked upon BERT models for specific sub-tasks. Our submissions achieve F1 scores of 91.7% in Sub-task A, 66.5% in Sub-task B, and 63.2% in Sub-task C. We perform an ablation study which reveals that domain tuning considerably improves the classification performance. Furthermore, error analysis shows common misclassification errors made by our model and outlines research directions for future.