1. Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models
- Author
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Duval, Alexandre, Lamson, Thomas, de Kerouara, Gael de Leseleuc, and Gallé, Matthias
- Subjects
Computer Science - Computation and Language - 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., Comment: Accepted at EACL 2021
- Published
- 2020