User's Intention Understanding in Question-Answering System Using Attention-based LSTM

Abstract

A rule-based question-answering system is limited in its ability to understand a user's intention due to the inevitable incompleteness of the rules. To address this problem, in this paper, we propose a method to estimate question type and question keyword class from a user's question by using an attention-based LSTM (Long Short-Term Memory) model. We also propose a joint model for simultaneous estimation of question type and question keyword class. Through the experiment, the effectiveness of our proposed method is evaluated based upon estimation rates. In addition, the proposed method for question type estimation is compared with a rule-based system, support vector machine (SVM), and Random Forest. The method for question keyword class estimation is also compared with the non-attention LSTM model and the conventional model.