Abstract
Low-resource polysynthetic languages pose many challenges in NLP tasks, such as morphological analysis and Machine Translation, due to available resources and tools, and the morphologically complex languages. This research focuses on the morphological segmentation while adapting an unsupervised approach based on Adaptor Grammars in low-resource setting. Experiments and evaluations on Inuinnaqtun, one of Inuit language family in Northern Canada, considered a language that will be extinct in less than two generations, have shown promising results.- Anthology ID:
- 2021.americasnlp-1.17
- Volume:
- Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Manuel Mager, Arturo Oncevay, Annette Rios, Ivan Vladimir Meza Ruiz, Alexis Palmer, Graham Neubig, Katharina Kann
- Venue:
- AmericasNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 159–162
- Language:
- URL:
- https://aclanthology.org/2021.americasnlp-1.17
- DOI:
- 10.18653/v1/2021.americasnlp-1.17
- Cite (ACL):
- Ngoc Tan Le and Fatiha Sadat. 2021. Towards a First Automatic Unsupervised Morphological Segmentation for Inuinnaqtun. In Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas, pages 159–162, Online. Association for Computational Linguistics.
- Cite (Informal):
- Towards a First Automatic Unsupervised Morphological Segmentation for Inuinnaqtun (Le & Sadat, AmericasNLP 2021)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2021.americasnlp-1.17.pdf