Constrained deep answer sentence selection

Abstract

In this paper, we propose Constrained Deep Neural Network (CDNN) a simple deep neural model for answer sentence selection. CDNN makes its predictions based on neural reasoning compound with some symbolic constraints. It integrates pattern matching technique into sentence vector learning. When trained using enough samples, CDNN outperforms regular models. We show how using other sources of training data as a mean of transfer learning can enhance the performance of the network. In a well-studied dataset for answer sentence selection, our network improves the state of the art in answer sentence selection significantly.