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:
- Bibkey:
- 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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.387.pdf
Export citation
@inproceedings{mompelat-etal-2022-parse,
title = "How to Parse a Creole: When Martinican Creole Meets {F}rench",
author = {Mompelat, Ludovic and
Dakota, Daniel and
K{\"u}bler, Sandra},
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.387",
pages = "4397--4406",
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.",
}
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%0 Conference Proceedings %T How to Parse a Creole: When Martinican Creole Meets French %A Mompelat, Ludovic %A Dakota, Daniel %A Kübler, Sandra %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F mompelat-etal-2022-parse %X 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. %U https://aclanthology.org/2022.coling-1.387 %P 4397-4406
Markdown (Informal)
[How to Parse a Creole: When Martinican Creole Meets French](https://aclanthology.org/2022.coling-1.387) (Mompelat et al., COLING 2022)
- How to Parse a Creole: When Martinican Creole Meets French (Mompelat et al., COLING 2022)
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.