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Controllable Text Generation (CTG) has obtained great success due to itsfine-grained generation ability obtained by focusing on multiple attributes.However, most existing CTG researches overlook how to utilize the attributeentanglement to enhance the diversity of the controlled generated texts. Facingthis dilemma, we focus on a novel CTG scenario, i.e., blessing generation whichis challenging because high-quality blessing texts require CTG models tocomprehensively consider the entanglement between multiple attributes (e.g.,objects and occasions). To promote the research on blessing generation, wepresent EBleT, a large-scale Entangled Blessing Text dataset containing 293KEnglish sentences annotated with multiple attributes. Furthermore, we proposenovel evaluation metrics to measure the quality of the blessing texts generatedby the baseline models we designed. Our study opens a new research directionfor controllable text generation and enables the development ofattribute-entangled CTG models. Our dataset and source codes are available at\url{https://github.com/huangshulin123/Blessing-Generation}.

Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation