Genre-Controllable Story Generation via Supervised Contrastive Learning

ABSTRACT While controllable text generation has received attention due to the recent advances in large-scale pre-trained language models, there is a lack of research that focuses on story-specific controllability. To address this, we present Story Control via Supervised Contrastive learning model (SCSC), to create a story conditioned on genre. For this, we design a supervised contrastive objective combined with log-likelihood objective, to capture the intrinsic differences among the stories in different genres. The results of our automated evaluation and user study demonstrate that the proposed method is effective in genre-controlled story generation.