EduNLP: Towards a Unified and Modularized Library for Educational Resources

TL;DR

EduNLP is a comprehensive, modular library designed to facilitate educational resource understanding by providing standardized workflows, customizable pipelines, and a collection of models and evaluation tasks tailored for the education domain.

Contribution

It introduces a unified, modular framework specifically for educational NLP tasks, integrating multiple models and evaluation tools in a configurable pipeline.

Findings

Provides 10 models across four categories for educational NLP.

Includes 5 downstream evaluation tasks on 8 subjects.

Enables customizable workflows for educational resource understanding.

Abstract

Educational resource understanding is vital to online learning platforms, which have demonstrated growing applications recently. However, researchers and developers always struggle with using existing general natural language toolkits or domain-specific models. The issue raises a need to develop an effective and easy-to-use one that benefits AI education-related research and applications. To bridge this gap, we present a unified, modularized, and extensive library, EduNLP, focusing on educational resource understanding. In the library, we decouple the whole workflow to four key modules with consistent interfaces including data configuration, processing, model implementation, and model evaluation. We also provide a configurable pipeline to unify the data usage and model usage in standard ways, where users can customize their own needs. For the current version, we primarily provide 10 typical models from four categories, and 5 common downstream-evaluation tasks in the education domain on 8 subjects for users' usage. The project is released at: https://github.com/bigdata-ustc/EduNLP.