论文标题
在自动诗歌中介绍创造力的方面
Introducing Aspects of Creativity in Automatic Poetry Generation
论文作者
论文摘要
诗歌的产生涉及教学系统，以自动生成类似于诗歌作品的文本。深度学习系统可以通过训练诗歌的培训并建模特定的语言风格来学习诗歌。在本文中，我们提出了一种方法，即对诗歌产生的下游任务进行微调GPT-2（一种预训练的语言模型）。我们通过引入创意元素来扩展有关诗歌产生的先前工作。具体来说，我们产生的诗歌在读者中表达情感并引起同样的诗，以及使用梦想的语言 - 称为梦想诗歌的诗。我们能够制作出正确引起悲伤和欢乐的情感的诗歌，分别为87.5％和85％。我们通过对描述梦想的文本训练进行培训来产生梦幻般的诗歌。该模型的诗歌显示出在李克特量表上以不少于3.2的成绩捕捉梦想诗的元素。我们为所有诗歌进行众包人类评估。我们还利用了COH-Metrix工具，概述了我们用来评估生成文本质量的指标。
Poetry Generation involves teaching systems to automatically generate text that resembles poetic work. A deep learning system can learn to generate poetry on its own by training on a corpus of poems and modeling the particular style of language. In this paper, we propose taking an approach that fine-tunes GPT-2, a pre-trained language model, to our downstream task of poetry generation. We extend prior work on poetry generation by introducing creative elements. Specifically, we generate poems that express emotion and elicit the same in readers, and poems that use the language of dreams---called dream poetry. We are able to produce poems that correctly elicit the emotions of sadness and joy 87.5 and 85 percent, respectively, of the time. We produce dreamlike poetry by training on a corpus of texts that describe dreams. Poems from this model are shown to capture elements of dream poetry with scores of no less than 3.2 on the Likert scale. We perform crowdsourced human-evaluation for all our poems. We also make use of the Coh-Metrix tool, outlining metrics we use to gauge the quality of text generated.
