On the other hand, recurrent neural networks may derive some of their predictive power in AES from more redundant signals in longer input sequences (as sketched by<cite> Taghipour and Ng (2016)</cite> ). As a result, the shorter responses in SAS may hinder the ability of recurrent networks to achieve state-of-the-art results. To explore the effectiveness of neural network architectures on SAS, we use the basic architecture and parameters of<cite> Taghipour and Ng (2016)</cite> on three publicly available short answer datasets: ASAP-SAS (Shermis, 2015), Powergrading (Basu et al., 2013) , and SRA (Dzikovska et al., 2016 (Dzikovska et al., , 2013 .