Automatic Expressive Opinion Sentence Generation for Enjoyable Conversational Systems

Abstract

In terms of functional conversations, Grice's Maxim of Quantity suggests that responses should contain no more information than was explicitly asked for. However, in our daily conversations, more informative response skills are usually employed in order to hold enjoyable conversations with interlocutors. These responses are usually produced as forms of one's additional opinions, which usually contain their original viewpoints as well as novel means of expression, rather than simple and common responses characteristic of the general public. In this paper, we propose automatic expressive opinion sentence generation mechanisms for enjoyable conversational systems. The generated opinions are extracted from a large number of reviews on the web, and ranked in terms of contextual relevance, length of sentences, and amount of information represented by the frequency of adjectives. The sentence generator also has an additional phrasing skill. Three controlled lab experiments were conducted, where subjects were requested to read generated sentences and watch videos filmed about conversations between the robot and a person. The results implied that mechanisms effectively promote users' enjoyment and interests.