Tables as Semi structured Knowledge for Question Answering
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 trade off. 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 semi structured 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 highly structured Markov Logic Network model.
ACL