Toxic Spans Detection
16 papers with code • 0 benchmarks • 0 datasets
Given a sentence identify the toxic spans present in it.
These leaderboards are used to track progress in Toxic Spans Detection
NLRG at SemEval-2021 Task 5: Toxic Spans Detection Leveraging BERT-based Token Classification and Span Prediction Techniques
In our paper, we explore simple versions of both of these approaches and their performance on the task.
We tackle this problem utilizing a combination of a state-of-the-art pre-trained language model (CharacterBERT) and a traditional bag-of-words technique.
In this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection.
In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms.
MIPT-NSU-UTMN at SemEval-2021 Task 5: Ensembling Learning with Pre-trained Language Models for Toxic Spans Detection
This paper describes our system for SemEval-2021 Task 5 on Toxic Spans Detection.
UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models
Detecting which parts of a sentence contribute to that sentence's toxicity -- rather than providing a sentence-level verdict of hatefulness -- would increase the interpretability of models and allow human moderators to better understand the outputs of the system.
Cisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments
We also explore a dependency parsing approach where we extract spans from the input sentence under the supervision of target span boundaries and rank our spans using a biaffine model.