Generation of Objections Using Topic and Claim Information in Debate Dialogue System

In recent years, systems with a dialogue interface are attracting wide attention [1, 2]. We propose a dialogue system that can debate with users about news broadcasts on TV or radio and help users to understand the meaning deeply. We previously reported a debate system that collected opinions from the Web [4], vectorized them, and finally selected the most appropriate supporting/opposing opinion among them for debating. In this paper, we propose a Neural Network Language Model that can generate objections instead selecting one opinion for debating. The model generates sentences by putting claim information (supporting/opposition) in the input layer of Long Short-Term Memory (LSTM) [3]. We conducted experiments by BLEU score and Human Evaluation, and both showed the effectiveness of our method.