We develop models to classify desirable evidence and desirable reasoning
revisions in student argumentative writing. We explore two ways to improve
classifier performance - using the essay context of the revision, and using the
feedback students received before the revision. We perform both intrinsic and
extrinsic evaluation for each of our models and report a qualitative analysis.
Our results show that while a model using feedback information improves over a
baseline model, models utilizing context - either alone or with feedback - are
the most successful in identifying desirable revisions.

本研究通过探索使用文章修订的上下文和反馈信息两种方式来提高分类器性能，以区分学生论述写作中的有意义的证据和合理推理演绎，并进行严格评估和定性分析，研究发现通过使用上下文来辨别有意义的修订是最成功的。

预测辩论写作中证据和推理的理想修改

Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing

We develop a system for the FEVER fact extraction and verification challenge
that uses a high precision entailment classifier based on transformer networks
pretrained with language modeling, to classify a broad set of potential
evidence. The precision of the entailment classifier allows us to enhance
recall by considering every statement from several articles to decide upon each
claim. We include not only the articles best matching the claim text by TFIDF
score, but read additional articles whose titles match named entities and
capitalized expressions occurring in the claim text. The entailment module
evaluates potential evidence one statement at a time, together with the title
of the page the evidence came from (providing a hint about possible pronoun
antecedents). In preliminary evaluation, the system achieves .5736 FEVER score,
.6108 label accuracy, and .6485 evidence F1 on the FEVER shared task test set.

本研究开发了一种基于 transformer 网络预训练的高精度蕴含分类器，用于对 FEVER 事实提取和验证挑战中的广泛潜在证据进行分类，从而提高了召回率；在初步评估中，该系统在 FEVER 公共数据集上取得了.5736 的 FEVER 得分、.6108 的标签准确性和.6485 的证据 F1 值。