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Tables extracted from web documents can be used to directly answer many websearch queries. Previous works on question answering (QA) using web tables havefocused on factoid queries, i.e., those answerable with a short string likeperson name or a number. However, many queries answerable using tables arenon-factoid in nature. In this paper, we develop an open-domain QA approachusing web tables that works for both factoid and non-factoid queries. Our keyinsight is to combine deep neural network-based semantic similarity between thequery and the table with features that quantify the dominance of the table inthe document as well as the quality of the information in the table. Ourexperiments on real-life web search queries show that our approachsignificantly outperforms state-of-the-art baseline approaches. Our solution isused in production in a major commercial web search engine and serves directanswers for tens of millions of real user queries per month.

Open Domain Question Answering Using Web Tables