Abstract
We investigate methods to develop a parser for Martinican Creole, a highly under-resourced language, using a French treebank. We compare transfer learning and multi-task learning models and examine different input features and strategies to handle the massive size imbalance between the treebanks. Surprisingly, we find that a simple concatenated (French + Martinican Creole) baseline yields optimal results even though it has access to only 80 Martinican Creole sentences. POS embeddings work better than lexical ones, but they suffer from negative transfer.- Anthology ID:
- 2022.coling-1.387
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4397–4406
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.387
- DOI:
- Cite (ACL):
- Ludovic Mompelat, Daniel Dakota, and Sandra Kübler. 2022. How to Parse a Creole: When Martinican Creole Meets French. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4397–4406, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- How to Parse a Creole: When Martinican Creole Meets French (Mompelat et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2022.coling-1.387.pdf