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。

基于神经网络的儿童早期写作自动反馈

Neural machine translation for automated feedback on children's  early-stage writing

Current approaches to novelty or anomaly detection are based on deep neural
networks. Despite their effectiveness, neural networks are also vulnerable to
imperceptible deformations of the input data. This is a serious issue in
critical applications, or when data alterations are generated by an adversarial
attack. While this is a known problem that has been studied in recent years for
the case of supervised learning, the case of novelty detection has received
very limited attention. Indeed, in this latter setting the learning is
typically unsupervised because outlier data is not available during training,
and new approaches for this case need to be investigated. We propose a new
prior that aims at learning a robust likelihood for the novelty test, as a
defense against attacks. We also integrate the same prior with a
state-of-the-art novelty detection approach. Because of the geometric
properties of that approach, the resulting robust training is computationally
very efficient. An initial evaluation of the method indicates that it is
effective at improving performance with respect to the standard models in the
absence and presence of attacks.

本文针对深度神经网络在检测新颖性或异常性时容易受到输入数据微小变形的问题，提出了一个旨在学习强大的新颖性检测正确性的先验；同时，将该先验与最先进的新颖性检测方法相结合，并评估了该方法在攻击存在与否的情况下的性能表现，得到了较好的结果。