  Education artificial intelligence aims to profit tasks in the education
domain such as intelligent test paper generation and consolidation exercises
where the main technique behind is how to match the exercises, known as the
finding similar exercises(FSE) problem. Most of these approaches emphasized
their model abilities to represent the exercise, unfortunately there are still
many challenges such as the scarcity of data, insufficient understanding of
exercises and high label noises. We release a Chinese education pre-trained
language model BERT$_{Edu}$ for the label-scarce dataset and introduce the
exercise normalization to overcome the diversity of mathematical formulas and
terms in exercise. We discover new auxiliary tasks in an innovative way depends
on problem-solving ideas and propose a very effective MoE enhanced multi-task
model for FSE task to attain better understanding of exercises. In addition,
confidence learning was utilized to prune train-set and overcome high noises in
labeling data. Experiments show that these methods proposed in this paper are
very effective.

Artificial Intelligence