| Popis: |
The dialogue response generation system is one of important topics in natural language processing, but the current system is difficult to produce human-like dialogues. The responses proposed by the chat-bot are only a passive answer or assentation, which does not arouse the desire of people to continue communicating. To address this challenge, in this paper, we propose a question generalization method with three types of question proposing schemes in different conversation patterns. A probability-triggered multiple conversion mechanism is used to control the system to actively propose different types of questions. In experiments, our proposed method demonstrates its effectiveness in dialogue response generalization on standard dataset. In addition, it achieves good performance in subjective conversational assessment. |
