论文标题
浪漫计算
Romantic-Computing
论文作者
论文摘要
在本文中，我们比较了各种文本生成模型以早期英国浪漫主义风格写诗歌的能力。这些模型包括：具有长期记忆的角色级复发性神经网络，拥抱Face的GPT-2，OpenAI的GPT-3和Eleutherai的GPT-NEO。质量是基于音节计数的，并与自动评估度量指标相干。与变压器模型相比，角色级复发性神经网络的表现差得多。而且，随着参数大小的增加，变压器模型的诗歌的质量得到改善。这些模型通常不会在创造性的环境中比较，我们很乐意做出贡献。
In this paper we compare various text generation models' ability to write poetry in the style of early English Romanticism. These models include: Character-Level Recurrent Neural Networks with Long Short-Term Memory, Hugging Face's GPT-2, OpenAI's GPT-3, and EleutherAI's GPT-NEO. Quality was measured based syllable count and coherence with the automatic evaluation metric GRUEN. Character-Level Recurrent Neural Networks performed far worse compared to transformer models. And, as parameter-size increased, the quality of transformer models' poems improved. These models are typically not compared in a creative context, and we are happy to contribute.
