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The greatest challenges in building sophisticated open-domain conversationalagents arise directly from the potential for ongoing mixed-initiativemulti-turn dialogues, which do not follow a particular plan or pursue aparticular fixed information need. In order to make coherent conversationalcontributions in this context, a conversational agent must be able to track thetypes and attributes of the entities under discussion in the conversation andknow how they are related. In some cases, the agent can rely on structuredinformation sources to help identify the relevant semantic relations andproduce a turn, but in other cases, the only content available comes fromsearch, and it may be unclear which semantic relations hold between the searchresults and the discourse context. A further constraint is that the system mustproduce its contribution to the ongoing conversation in real-time. This paperdescribes our experience building SlugBot for the 2017 Alexa Prize, anddiscusses how we leveraged search and structured data from different sources tohelp SlugBot produce dialogic turns and carry on conversations whose lengthover the semi-finals user evaluation period averaged 8:17 minutes.

Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue