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

作者：Alexandre Duval, Thomas Lamson, Gael de Leseleuc de Kerouara, Matthias Gallé · 发表于：Conference of the European Chapter of the Association for Computational Linguistics · 年份：2021 · DOI：10.18653/v1/2021.eacl-demos.33 · 被引用次数：16 · 研究领域：Computer Science

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.