Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
| Publication date | 1/06/2021 |
|---|---|
| Host publication | NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations |
| Publisher | Association for Computational Linguistics |
| Pages | 144-152 |
| Number of pages | 9 |
| ISBN (electronic) | 9781954085480 |
| <mark>Original language</mark> | English |
| Event | 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics - Virtual Conference Duration: 6/06/2021 → 11/06/2021 https://2021.naacl.org |
| Conference | 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics |
|---|---|
| Abbreviated title | NAACL2021 |
| Period | 6/06/21 → 11/06/21 |
| Internet address |
| Name | NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations |
|---|
| Conference | 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics |
|---|---|
| Abbreviated title | NAACL2021 |
| Period | 6/06/21 → 11/06/21 |
| Internet address |
The interest in offensive content identification in social media has grown substantially in recent years. Previous work has dealt mostly with post level annotations. However, identifying offensive spans is useful in many ways. To help coping with this important challenge, we present MUDES, a multilingual system to detect offensive spans in texts. MUDES features pre-trained models, a Python API for developers, and a user-friendly web-based interface. A detailed description of MUDES' components is presented in this paper.