Automatic short answer grading is an important research direction in the
exploration of how to use artificial intelligence (AI)-based tools to improve
education. Current state-of-the-art approaches use neural language models to
create vectorized representations of students responses, followed by
classifiers to predict the score. However, these approaches have several key
limitations, including i) they use pre-trained language models that are not
well-adapted to educational subject domains and/or student-generated text and
ii) they almost always train one model per question, ignoring the linkage
across a question and result in a significant model storage problem due to the
size of advanced language models. In this paper, we study the problem of
automatic short answer grading for students' responses to math questions and
propose a novel framework for this task. First, we use MathBERT, a variant of
the popular language model BERT adapted to mathematical content, as our base
model and fine-tune it for the downstream task of student response grading.
Second, we use an in-context learning approach that provides scoring examples
as input to the language model to provide additional context information and
promote generalization to previously unseen questions. We evaluate our
framework on a real-world dataset of student responses to open-ended math
questions and show that our framework (often significantly) outperforms
existing approaches, especially for new questions that are not seen during
training.

本文研究了自动短答案评分问题，提出了一种新的基于 MathBERT 及上下文学习方法的框架，并在真实数据集上进行了评估，证明该框架对于之前未见过的数学问题的表现优于现有方法。

基于上下文元学习的自动短数学答案评分

Automatic Short Math Answer Grading via In-context Meta-learning

Large-scale pre-trained models like BERT, have obtained a great success in
various Natural Language Processing (NLP) tasks, while it is still a challenge
to adapt them to the math-related tasks. Current pre-trained models neglect the
structural features and the semantic correspondence between formula and its
context. To address these issues, we propose a novel pre-trained model, namely
\textbf{MathBERT}, which is jointly trained with mathematical formulas and
their corresponding contexts. In addition, in order to further capture the
semantic-level structural features of formulas, a new pre-training task is
designed to predict the masked formula substructures extracted from the
Operator Tree (OPT), which is the semantic structural representation of
formulas. We conduct various experiments on three downstream tasks to evaluate
the performance of MathBERT, including mathematical information retrieval,
formula topic classification and formula headline generation. Experimental
results demonstrate that MathBERT significantly outperforms existing methods on
all those three tasks. Moreover, we qualitatively show that this pre-trained
model effectively captures the semantic-level structural information of
formulas. To the best of our knowledge, MathBERT is the first pre-trained model
for mathematical formula understanding.

提出了一种新的预训练模型 MathBERT，该模型通过联合训练数学公式及其上下文信息，并根据操作树中提取的掩码公式子结构进行设计，以捕捉公式的语义结构特征，同时在数学信息检索、公式主题分类和公式标题生成等三个任务中取得了显著的超出现有方法的表现