Abstract
We present a research narrative aimed at enabling language technology for multiple natural language generation (NLG) tasks in low-resource languages (LRLs). With approximately 7,000 languages spoken globally, many lack the resources required for model training. NLG applications for LRLs present two additional key challenges: (i) The training is more pronounced, and (ii) Zero-shot modeling is a viable research direction for scalability; however, generating zero-shot well-formed text in target LRLs is challenging. Addressing these concerns, this narrative introduces three promising research explorations that serve as a step toward enabling language technology for many LRLs. These approaches make effective use of transfer learning and limited supervision techniques for modeling. Evaluations were conducted mostly in the zero-shot setting, enabling scalability. This research narrative is an ongoing doctoral thesis.- Anthology ID:
- 2023.bigpicture-1.7
- Volume:
- Proceedings of the Big Picture Workshop
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Yanai Elazar, Allyson Ettinger, Nora Kassner, Sebastian Ruder, Noah A. Smith
- Venue:
- BigPicture
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 80–92
- Language:
- URL:
- https://aclanthology.org/2023.bigpicture-1.7
- DOI:
- 10.18653/v1/2023.bigpicture-1.7
- Cite (ACL):
- Kaushal Maurya and Maunendra Desarkar. 2023. Towards Low-resource Language Generation with Limited Supervision. In Proceedings of the Big Picture Workshop, pages 80–92, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Towards Low-resource Language Generation with Limited Supervision (Maurya & Desarkar, BigPicture 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.bigpicture-1.7.pdf