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.
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.
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