A Two-Layer Dialogue Framework For Authoring Social Bots
Published in 1st Proceedings of Alexa Prize (Alexa Prize 2017), 2017
Jieming Ji, Qingyun Wang, Zev Battad, Jiashun Gou, Jingfei Zhou, Rahul Divekar, Craig Carlson, Mei Si (2017). 1st Proceedings of Alexa Prize. (Alexa Prize 2017).
[Alexa Prize Proceedings] [Code]
Abstract
In this work, we explored creating a social bot for casual conversations. One of the major challenges in designing social bots is how to keep the user engaged. We experimented with a range of conversational activities, such as providing news and playing games, and strategies for controlling the dialogue flow. To support these experiments, we proposed a two-layer dialogue framework which allows for flexible reuse and reorganization of individual dialogue modules. The chat-bot was deployed as an Amazon Alexia Skill, and participated the Alexia social bot competition. Over 20k Alexia users interacted with and rated our bot between 4/1/2017 and 8/26/2017. We found that in general supporting a richer set of conversational activities is desirable, and the users are more in favor of having natural conversations over menu-based conversations. Our results also indicate that the lengths of interactions with the entertainment-oriented modules positively correspond to the users’ ratings of the bot. In contrast, for modules that serve as an information provider, i.e., news and news comments the lengths of the interactions do not predict the ratings.