CLApr 20, 2021
UIT-ISE-NLP at SemEval-2021 Task 5: Toxic Spans Detection with BiLSTM-CRF and ToxicBERT Comment Classification
arXiv:2104.10100v4713 citations
Originality Synthesis-oriented
AI Analysis
This work addresses the need for automated content moderation in online platforms, but it is incremental as it builds on existing methods for a specific competition task.
The paper tackled the problem of detecting toxic words in online posts by developing a model combining BiLSTM-CRF and ToxicBERT classification, achieving an F1-score of 62.23% on the SemEval-2021 Task 5 benchmark.
We present our works on SemEval-2021 Task 5 about Toxic Spans Detection. This task aims to build a model for identifying toxic words in whole posts. We use the BiLSTM-CRF model combining with ToxicBERT Classification to train the detection model for identifying toxic words in posts. Our model achieves 62.23% by F1-score on the Toxic Spans Detection task.