Development of a Selection Mechanism Using Deep Auto-Regression Model with Examples of Seasonal Fixed-form Haiku in Japanese

Abstract

A selection mechanism for Japanese haiku, world's smallest fixed form of poetry, is developed to select haiku of interest to the user from a set of haiku generated by a deep autoregressive model. This is achieved by training a deep model that estimates the probability of occurrence or similarity of the haiku to be selected by learning the user's previous haiku works. 100 million haiku are generated and selected using a large-scale language model that has additionally learned 400,000 haiku data. We additionally train a deep language model using several thousand haiku created by users in the past as training data, and decide which haiku to select from the acquired model. With the cooperation of haiku poets, we evaluated the effectiveness of the autoregressive model and the masked language model by presenting the selection results with different numbers of parameters. The experimental results revealed the high performance of the autoregressive model and the importance of using the ratio of the estimated results of the model trained only on the case data and the model trained on haiku in general, rather than simply selecting the haiku with the highest estimated probability of occurrence.