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In this paper, we describe a dataset and baseline result for a questionanswering that utilizes web tables. It contains commonly asked questions on theweb and their corresponding answers found in tables on websites. Our dataset isnovel in that every question is paired with a table of a different signature.In particular, the dataset contains two classes of tables: entity-instancetables and the key-value tables. Each QA instance comprises a table of eitherkind, a natural language question, and a corresponding structured SQL query. Webuild our model by dividing question answering into several tasks, includingtable retrieval and question element classification, and conduct experiments tomeasure the performance of each task. We extract various features specific toeach task and compose a full pipeline which constructs the SQL query from itsparts. Our work provides qualitative results and error analysis for each task,and identifies in detail the reasoning required to generate SQL expressionsfrom natural language questions. This analysis of reasoning informs futuremodels based on neural machine learning.

Question Answering via Web Extracted Tables and Pipelined Models