Genre-Controllable Story Generation via Supervised Contrastive Learning
Jin-Uk Cho, Min-Su Jeong, JinYeong Bak, Yun-Gyung Cheong · Proceedings of the ACM Web Conference 2022 · 2022
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.