论文标题
开放式文本生成的活动过渡计划
Event Transition Planning for Open-ended Text Generation
论文作者
论文摘要
开放式文本生成任务（例如对话生成和故事完成）需要模型在前面有限的上下文中产生连贯的延续。这些任务的开放性质为如今的神经自动回归文本生成器带来了新的挑战。尽管这些神经模型擅长产生类似人类的文本，但他们很难安排给定的事实与可能发生的事件之间的因果关系和关系。为了弥合这一差距，我们提出了一种新颖的两阶段方法，该方法明确地安排了开放式文本生成中随之而来的事件。我们的方法可以理解为一种经过特殊训练的粗到精细算法，其中事件过渡计划者在第二阶段提供了“粗”图骨架和文本生成器，可以完善骨骼。对两个开放式文本生成任务进行的实验表明，我们提出的方法有效地提高了生成的文本的质量，尤其是在连贯性和多样性方面。该代码可在：\ url {https://github.com/qtli/eventplanfortextgen}中获得。
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}.
