Most NLG is Low-Resource: here's what we can do about it

Howcroft, David M.; Gkatzia, Dimitra

Abstract

Many domains and tasks in natural language generation (NLG) are inherently 'low-resource', where training data, tools and linguistic analyses are scarce. This poses a particular challenge to researchers and system developers in the era of machine-learning-driven NLG. In this position paper, we initially present the challenges researchers & developers often encounter when dealing with low-resource settings in NLG. We then argue that it is un-sustainable to collect large aligned datasets or build large language models from scratch for every possible domain due to cost, labour, and time constraints, so researching and developing methods and resources for low-resource settings is vital. We then discuss current approaches to low-resource NLG, followed by proposed solutions and promising avenues for future work in NLG for low-resource settings.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
• Start Date: Dec 7, 2022
• Acceptance Date: Oct 24, 2022
• Publication Date: Dec 7, 2022
• Deposit Date: Jun 9, 2023
• Publicly Available Date: Jun 12, 2023
• Publisher: Association for Computational Linguistics (ACL)
• Pages: 336-350
• Book Title: Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
• Publisher URL: https://aclanthology.org/2022.gem-1.29/