- Aasish Pappu, Alexander I. Rudnicky. 2014. Learning situated knowledge bases through dialog. Abstract: To respond to a user’s query, dialog agents can use a knowledge base that is either domain specific, commonsense (e.g., NELL, Freebase) or a combination of both. The drawback is that domain-specific knowledge bases will likely be limited and static; commonsense ones are dynamic but contain general information found on the web and will be sparse with respect to a domain. We address this issue through a system that solicits situational information from its users in a domain that provides information on events (seminar talks) to augment its knowledge base (covering an academic field). We find that this knowledge is consistent and useful and that it provides reliable information to users. We show that, in comparison to a base system, users find that retrievals are more relevant when the system uses its informally acquired knowledge to augment their queries.
