Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models
Abstract
It is standard procedure these days to solve Information Extraction task by fine-tuning large pre-trained language models. This is not the case for generation task, which relies on a variety of techniques for controlled language generation. In this paper, we describe a system that fine-tunes a natural language generation model for the problem of solving Writer's Block. The fine-tuning changes the conditioning to also include the right context in addition to the left context, as well as an optional list of entities, the size, the genre and a summary of the paragraph that the human author wishes to generate. Our proposed fine-tuning obtains excellent results, even with a small number of epochs and a total cost of USD 150. The system can be accessed as a web-service, and all the code is released. A video showcasing the interface and the model is also available.
Keywords
Cite
@article{arxiv.2101.03216,
title = {Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models},
author = {Alexandre Duval and Thomas Lamson and Gael de Leseleuc de Kerouara and Matthias Gallé},
journal= {arXiv preprint arXiv:2101.03216},
year = {2021}
}
Comments
Accepted at EACL 2021