K-12BERT: BERT for K-12 education
This addresses the need for tailored NLP tools in K-12 education platforms, but it is incremental as it adapts existing methods to a new domain.
The authors tackled the lack of a domain-specific language model for K-12 education by training K-12BERT on a curated corpus across multiple subjects, achieving evaluation on downstream tasks like hierarchical taxonomy tagging.
Online education platforms are powered by various NLP pipelines, which utilize models like BERT to aid in content curation. Since the inception of the pre-trained language models like BERT, there have also been many efforts toward adapting these pre-trained models to specific domains. However, there has not been a model specifically adapted for the education domain (particularly K-12) across subjects to the best of our knowledge. In this work, we propose to train a language model on a corpus of data curated by us across multiple subjects from various sources for K-12 education. We also evaluate our model, K12-BERT, on downstream tasks like hierarchical taxonomy tagging.