Event Transition Planning for Open-ended Text Generation

• URL: http://arxiv.org/abs/2204.09453v1
• Date: Wed, 20 Apr 2022 13:37:51 GMT
• Title: Event Transition Planning for Open-ended Text Generation
• Authors: Qintong Li, Piji Li, Wei Bi, Zhaochun Ren, Yuxuan Lai, Lingpeng Kong
• Abstract summary: Open-ended text generation tasks require models to generate a coherent continuation given limited preceding context. 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.
• Score: 55.729259805477376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context. The open-ended 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: \url{https://github.com/qtli/EventPlanforTextGen}.

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