Before revision:  The proposed system is applied to the task of accept/reject prediction on the PeerRead dataset and compared against a recent BiLSTM-based model and joint textual+visual model . It gains 4.7\% accuracy over the best of both models on the computation and language domain and loses 2.4\% against the best of both on the machine learning domain . Compared to plain HANs, accuracy increases on both domains, with 1.5\% and 2\% respectively .
After revision:  The proposed system is applied to the task of accept/reject prediction on the PeerRead dataset and compared against a recent BiLSTM-based model and joint textual+visual model as well as against plain HANs . Compared to plain HANs, accuracy increases on both domains, with 1.5\% and 2\% respectively .