STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation
This work addresses the challenge of reliable evaluation for long-form creative text generation, which is a domain-specific problem for researchers in natural language processing and story generation.
The authors tackled the problem of evaluating story generation models by introducing a dataset and platform built from an online storytelling community, which includes 6K stories with fine-grained annotations and enables real authors to interact with models, showing that automatic metrics from edits correlate well with user feedback.
Systems for story generation are asked to produce plausible and enjoyable stories given an input context. This task is underspecified, as a vast number of diverse stories can originate from a single input. The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text. To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community. Our author-generated dataset contains 6K lengthy stories (125M tokens) with fine-grained natural language annotations (e.g., character goals and attributes) interspersed throughout each narrative, forming a robust source for guiding models. We evaluate language models fine-tuned on our dataset by integrating them onto STORIUM, where real authors can query a model for suggested story continuations and then edit them. Automatic metrics computed over these edits correlate well with both user ratings of generated stories and qualitative feedback from semi-structured user interviews. We release both the STORIUM dataset and evaluation platform to spur more principled research into story generation.