hub at SemEval-2021 Task 5: Toxic Span Detection Based on Word-Level Classification

Bo Huang, Yang Bai, Xiaobing Zhou


Abstract
This article introduces the system description of the hub team, which explains the related work and experimental results of our team’s participation in SemEval 2021 Task 5: Toxic Spans Detection. The data for this shared task comes from some posts on the Internet. The task goal is to identify the toxic content contained in these text data. We need to find the span of the toxic text in the text data as accurately as possible. In the same post, the toxic text may be one paragraph or multiple paragraphs. Our team uses a classification scheme based on word-level to accomplish this task. The system we used to submit the results is ALBERT+BILSTM+CRF. The result evaluation index of the task submission is the F1 score, and the final score of the prediction result of the test set submitted by our team is 0.6640226029.
Anthology ID:
2021.semeval-1.122
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
904–908
Language:
URL:
https://aclanthology.org/2021.semeval-1.122
DOI:
10.18653/v1/2021.semeval-1.122
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.122.pdf