Abstract
Text sanitization is the task of detecting and removing personal information from the text. While it has been well-studied in monolingual settings, today, there is also a need for multilingual text sanitization. In this paper, we introduce MultiLeg: a parallel, multilingual named entity (NE) dataset consisting of documents from the Court of Justice of the European Union annotated with semantic categories suitable for text sanitization. The dataset is available in 8 languages, and it contains 3082 parallel text segments for each language. We also show that the pseudonymized dataset remains useful for downstream tasks.- Anthology ID:
- 2024.lrec-main.1028
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 11776–11782
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1028
- DOI:
- Cite (ACL):
- Rinalds Vīksna and Inguna Skadiņa. 2024. MultiLeg: Dataset for Text Sanitisation in Less-resourced Languages. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11776–11782, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- MultiLeg: Dataset for Text Sanitisation in Less-resourced Languages (Vīksna & Skadiņa, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.1028.pdf