Machine-Learned Ranking Based Non-Task-Oriented Dialogue Agent Using Twitter Data

摘要

This paper describes a method for developing a non-task-oriented dialogue agent (also called chat-oriented or conversational dialogue agents) that can cover broad range of topics. Our method extracts a topic from a user's utterance and acquires candidate utterances that contain the topic from Twitter. Our agent selects a suitable utterance for dialogue context from candidates using machine-learned ranking method. Results of an experiment demonstrate that a dialogue agent based on the proposed method can conduct more natural and enjoyable conversation compared to other dialogue agents.