An empirical study on incorporating prior knowledge into BLSTM framework in answer selection

Abstract

Deep learning has become the state-of the art solution to answer selection. One distinguishing advantage of deep learning is that it avoids manual engineering via its end-to-end structure. But in the literature, substantial practices of introducing prior knowledge into the deep learning process are still observed with positive effect. Following this thread, this paper investigates the contribution of incorporating different prior knowledge into deep learning via an empirical study. Under a typical BLSTM framework, 3 levels, totaling 27 features are jointly integrated into the answer selection task. Experiment result confirms that incorporating prior knowledge can enhances the model, and different levels of linguistic features can improve the performance consistantly.