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arXiv · 2101.03216

Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models

Alexandre Duval·Thomas Lamson·Gael de Leseleuc de Kerouara·Matthias Gallé
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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.

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Alexandre Duval, Thomas Lamson, Gael de Leseleuc de Kerouara, Matthias Gallé. 2020-12-19. Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models. https://arxiv.org/abs/2101.03216

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