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