arXiv:1805.04833cs.CL
Hierarchical Neural Story Generation
Abstract
We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.
And convolution-based models still find use in all sorts of cool applications in language, such as: https://arxiv.org/abs/1805.04833 With regards to adversarial discussions, it's one thing to argue about whether method A or method B gives better results in a largely empirical…
phowonView on HNParent context