Abstract
This paper describes the structure and findings of the SIGTYP 2023 shared task on cognate and derivative detection for low-resourced languages, broken down into a supervised and unsupervised sub-task. The participants were asked to submit the test data's final prediction. A total of nine teams registered for the shared task where seven teams registered for both sub-tasks. Only two participants ended up submitting system descriptions, with only one submitting systems for both sub-tasks. While all systems show a rather promising performance, all could be within the baseline score for the supervised sub-task. However, the system submitted for the unsupervised sub-task outperforms the baseline score.
| Lingua originale | Inglese |
|---|---|
| Titolo della pubblicazione ospite | Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP |
| Editore | Association for Computational Linguistics |
| Pagine | 126-131 |
| Numero di pagine | 6 |
| ISBN (stampa) | 978-1-959429-56-2 |
| Stato di pubblicazione | Pubblicato - 2023 |
All Science Journal Classification (ASJC) codes
- Lingua e Linguistica
- Intelligenza Artificiale
- Interazione Uomo-Macchina
- Linguistica e Lingue
Keywords
- cognate detection
- low-resourced languages
- supervised learning
- unsupervised learning