  Tabular data is arguably one of the most commonly used data structures in
various practical domains, including finance, healthcare and e-commerce. The
inherent heterogeneity allows tabular data to store rich information. However,
based on a recently published tabular benchmark, we can see deep neural
networks still fall behind tree-based models on tabular datasets. In this
paper, we propose Trompt--which stands for Tabular Prompt--a novel architecture
inspired by prompt learning of language models. The essence of prompt learning
is to adjust a large pre-trained model through a set of prompts outside the
model without directly modifying the model. Based on this idea, Trompt
separates the learning strategy of tabular data into two parts. The first part,
analogous to pre-trained models, focus on learning the intrinsic information of
a table. The second part, analogous to prompts, focus on learning the
variations among samples. Trompt is evaluated with the benchmark mentioned
above. The experimental results demonstrate that Trompt outperforms
state-of-the-art deep neural networks and is comparable to tree-based models.
