Tables as Semi-structured Knowledge for Question Answering
Sunil Kumar Jauhar, Peter D. Turney, Eduard H. Hovy · 2016
Question answering requires access to a knowledge base to check facts and reason about information.Knowledge in the form of natural language text is easy to acquire, but difficult for automated reasoning.Highly-structured knowledge bases can facilitate reasoning, but are difficult to acquire.In this paper we explore tables as a semi-structured formalism that provides a balanced compromise to this tradeoff.We first use the structure of tables to guide the construction of a dataset of over 9000 multiple-choice questions with rich alignment annotations, easily and efficiently via crowd-sourcing.We then use this annotated data to train a semistructured feature-driven model for question answering that uses tables as a knowledge base.In benchmark evaluations, we significantly outperform both a strong unstructured retrieval baseline and a highlystructured Markov Logic Network model.