Implementation of Autoregressive Language Models for Generation of Seasonal Fixed-form Haiku in Japanese

Abstract

This paper describes the implementation of an artificial intelligence haiku generator. We trained language models using existing haiku and literary studies, evaluated model performance using automatically computable evaluation indices such as perplexity, and subjectively evaluated the generated haiku by using a questionnaire. The main contributions of this paper are as follows. First, the effectiveness of a series of model evaluation processes, including automatically calculable evaluation indices and the results of subjective evaluations using questionnaires, is investigated. These processes are effective in the development of haiku generation models. Second, high-quality haiku generation is achieved using high-performance language models such as GPT-2 and BART. The results of the questionnaire survey revealed that it is possible to generate sensible haiku comparable to those written by humans. The insight gained from this study is applicable to other generative tasks.