Peter W. Foltz

Swarnadeep Saha, Tejas I. Dhamecha, Smit Marvaniya et al.

One of the fundamental challenges towards building any intelligent tutoring system is its ability to automatically grade short student answers. A typical automatic short answer grading system (ASAG) grades student answers across multiple domains (or subjects). Grading student answers requires building a supervised machine learning model that evaluates the similarity of the student answer with the reference answer(s). We observe that unlike typical textual similarity or entailment tasks, the notion of similarity is not universal here. On one hand, para-phrasal constructs of the language can indicate similarity independent of the domain. On the other hand, two words, or phrases, that are not strict synonyms of each other, might mean the same in certain domains. Building on this observation, we propose JMD-ASAG, the first joint multidomain deep learning architecture for automatic short answer grading that performs domain adaptation by learning generic and domain-specific aspects from the limited domain-wise training data. JMD-ASAG not only learns the domain-specific characteristics but also overcomes the dependence on a large corpus by learning the generic characteristics from the task-specific data itself. On a large-scale industry dataset and a benchmarking dataset, we show that our model performs significantly better than existing techniques which either learn domain-specific models or adapt a generic similarity scoring model from a large corpus. Further, on the benchmarking dataset, we report state-of-the-art results against all existing non-neural and neural models.

Nicholas Dronen, Peter W. Foltz, Kyle Habermehl

Automated writing evaluation (AWE) has been shown to be an effective mechanism for quickly providing feedback to students. It has already seen wide adoption in enterprise-scale applications and is starting to be adopted in large-scale contexts. Training an AWE model has historically required a single batch of several hundred writing examples and human scores for each of them. This requirement limits large-scale adoption of AWE since human-scoring essays is costly. Here we evaluate algorithms for ensuring that AWE models are consistently trained using the most informative essays. Our results show how to minimize training set sizes while maximizing predictive performance, thereby reducing cost without unduly sacrificing accuracy. We conclude with a discussion of how to integrate this approach into large-scale AWE systems.