论文标题
K-12bert：Bert for K-12教育
K-12BERT: BERT for K-12 education
论文作者
论文摘要
在线教育平台由各种NLP管道提供动力，该管道利用BERT等模型来帮助内容策划。自从伯特（Bert）等预先训练的语言模型成立以来，还为将这些预训练的模型调整为特定领域而做出了许多努力。但是，据我们所知，没有一个专门针对教育领域（尤其是K-12）的模型。在这项工作中，我们建议对我们在K-12教育的各个来源的多个受试者策划的数据语料库培训语言模型。我们还评估了我们的模型K12-Bert，以等级分类标记等下游任务。
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.
