Event Transition Planning for Open-ended Text Generation
Qintong Li, Piji Li, Wei Bi, Zhaochun Ren, Yuxuan Lai, Lingpeng Kong · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context.The openended nature of these tasks brings new challenges to the neural auto-regressive text generators nowadays.Despite these neural models are good at producing human-like text, it is difficult for them to arrange causalities and relations between given facts and possible ensuing events.To bridge this gap, we propose a novel two-stage method which explicitly arranges the ensuing events in open-ended text generation.Our approach can be understood as a specially-trained coarse-to-fine algorithm, where an event transition planner provides a "coarse" plot skeleton and a text generator in the second stage refines the skeleton.Experiments on two open-ended text generation tasks demonstrate that our proposed method effectively improves the quality of the generated text, especially in coherence and diversity.The code is available at: https://github.com/ qtli/EventPlanforTextGen.