  Toxicity detection of text has been a popular NLP task in the recent years.
In SemEval-2021 Task-5 Toxic Spans Detection, the focus is on detecting toxic
spans within passages. Most state-of-the-art span detection approaches employ
various techniques, each of which can be broadly classified into Token
Classification or Span Prediction approaches. In our paper, we explore simple
versions of both of these approaches and their performance on the task.
Specifically, we use BERT-based models -- BERT, RoBERTa, and SpanBERT for both
approaches. We also combine these approaches and modify them to bring
improvements for Toxic Spans prediction. To this end, we investigate results on
four hybrid approaches -- Multi-Span, Span+Token, LSTM-CRF, and a combination
of predicted offsets using union/intersection. Additionally, we perform a
thorough ablative analysis and analyze our observed results. Our best
submission -- a combination of SpanBERT Span Predictor and RoBERTa Token
Classifier predictions -- achieves an F1 score of 0.6753 on the test set. Our
best post-eval F1 score is 0.6895 on intersection of predicted offsets from
top-3 RoBERTa Token Classification checkpoints. These approaches improve the
performance by 3% on average than those of the shared baseline models -- RNNSL
and SpaCy NER.
