[jkdufair]

I have a grant where were are doing just that. Implementing more or less SOTA research using fairly vanilla LSTM networks from 2-3 years ago (primarily Taghipour & Ng) to provide low stakes feedback to students on their essays in one of our teaching tools at Purdue. It’s based on research using the Kaggle ASAP database and we have found it to be pretty accurate across a variety of domains in early testing. Though some essay prompts seem to do better with CNNs vs. RNNs. I doubt many of the systems in TFA are based on LSTMs or neural nets at all. They are probably doing regression on hand-crafted features.

[ssivark]

Very interesting. Are there any meta-analyses / reviews that summarize progress in this area? Would it be possible to share your grant proposal -- I'd be curious to get an idea of what is being attempted.

[jkdufair]

It's an internal grant and I'm not sure I'd be allowed to share it. We are adding AES to our peer-review app. Currently as an additional "grader" to the peer reviews since that's what the PI requested. Since the tool allows unlimited submissions until the review date, I hope to add it as a "pre-flight" estimate to give students a chance to get a rough prediction of the score they will receive and a metric they can use as they revise until the due date.

I'm not aware of any meta-analyses myself. I have been keeping up with the ASAP competition and various attempts to improve on the initial systems for a number of years. The two papers I believe are having the most success are [1] and [2]. [3] seems promising for balancing the opposing forces of high accuracy for true positives and the risk of false positives via adversarially crafted inputs.

I'm also vaguely aware of research happening around extracting features from neural nets. I'd love to be able to help students understand why the system is predicting a particular score.

[1] https://www.aclweb.org/anthology/D16-1193 [2] https://arxiv.org/pdf/1606.04289.pdf [3] https://arxiv.org/pdf/1804.06898.pdf