GPoeT-2: A GPT-2 Based Poem Generator
This work addresses the challenge of automated poetry generation for creative applications, but it is incremental as it builds on existing language models.
The authors tackled the problem of generating structured poetry by fine-tuning GPT-2 to produce limericks with an AABBA rhyming scheme, resulting in a collection of 94 limericks that scored highly on automated metrics for quality.
This project aims to produce the next volume of machine-generated poetry, a complex art form that can be structured and unstructured, and carries depth in the meaning between the lines. GPoeT-2 is based on fine-tuning a state of the art natural language model (i.e. GPT-2) to generate limericks, typically humorous structured poems consisting of five lines with a AABBA rhyming scheme. With a two-stage generation system utilizing both forward and reverse language modeling, GPoeT-2 is capable of freely generating limericks in diverse topics while following the rhyming structure without any seed phrase or a posteriori constraints.Based on the automated generation process, we explore a wide variety of evaluation metrics to quantify "good poetry," including syntactical correctness, lexical diversity, and subject continuity. Finally, we present a collection of 94 categorized limericks that rank highly on the explored "good poetry" metrics to provoke human creativity.