In this work, we address the problem of assessing and constructing feedback
for early-stage writing automatically using machine learning. Early-stage
writing is typically vastly different from conventional writing due to phonetic
spelling and lack of proper grammar, punctuation, spacing etc. Consequently,
early-stage writing is highly non-trivial to analyze using common linguistic
metrics. We propose to use sequence-to-sequence models for "translating"
early-stage writing by students into "conventional" writing, which allows the
translated text to be analyzed using linguistic metrics. Furthermore, we
propose a novel robust likelihood to mitigate the effect of noise in the
dataset. We investigate the proposed methods using a set of numerical
experiments and demonstrate that the conventional text can be predicted with
high accuracy.

使用机器学习自动评估和构建早期写作的反馈，通过序列到序列模型将学生的早期写作 “翻译” 成 “传统” 写作，从而能够使用语言度量对翻译后的文本进行分析，并使用鲁棒似然方法减轻数据集中的噪声效应，并通过一系列数值实验证明能以高准确率预测传统文本。