Before revision: Complex language modelstrained on huge text corpora have shown unparalleled text generation capabilities, and thanks to transfer learning, are accessible to a greater number. However, despite recent developments, users are not yet able to fully control particular aspects of the text produced. This is why we propose a finetuned OpenAI GPT-2 model for controllable and contextualised text generation specific to novels. By integrating it into a web-service , we would like to enable authors to write and ask for automatic text generation which is consistent with both previous and next paragraphs.
After revision: 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 , we would like to enable authors to write and ask for automatic text generation which is consistent with both previous and next paragraphs.