Speaker: Peter Jansen, School of Information, University of Arizona
Presentation Title: What's in an Explanation? Toward Explanation-centered Inference for Science Exams
Abstract: Modern question answering systems are able to provide answers to a set of common natural language questions, but their ability to answer complex questions, or provide compelling explanations or justifications for why their answers are correct is still quite limited. These limitations are major barriers in high-impact domains like science and medicine, where the cost of making errors is high, and user trust is paramount. In this talk I'll discuss our recent work in developing systems that can build explanations to answer questions by aggregating information from multiple sources (sometimes called multi-hop inference). Aggregating information is challenging, particularly as the amount of information becomes large due to "semantic drift", or the tendency for inference algorithms to quickly move off-topic when assembling long chains of knowledge. Motivated by our earlier efforts in attempting to latently learn information aggregation for explanation generation (which is currently limited to short inference chains), I will discuss our current efforts to build a large corpus of detailed explanations expressed as lexically-connected explanation graphs to serve as training data for the multi-hop inference task. We will discuss characterizing what's in a science exam explanation, difficulties and methods for large-scale construction of detailed explanation graphs, and the possibility of automatically extracting common explanatory patterns from corpora such as this to support building large explanations (i.e. six or more aggregated facts) for unseen questions through merging, adapting, and adding to known explanatory patterns.