Abstract
We propose a simple yet effective information retrieval based approach to answer complex questions with open domain web tables. Specifically, given a question and a table, we rank all table cells based on their representations, and select the cells of the highest ranking score as the answer. To represent a cell, we design rich features which leverage both the semantic information of the question and the structure information of the table. The experiments are conducted on WIKITABLEQUESTIONS dataset in which the questions have complex semantics. Compared to a semantic parsing based method, our approach improves the accuracy score by 6.03 points.
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Notes
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A relation can be >, <, \(=\) or \(\ne \).
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An attribute can be an attribute in the table, or Count which counts the number of rows that a cell value appears in, or Index which returns the row index of a cell.
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Bao, J., Duan, N., Zhou, M., Zhao, T. (2018). An Information Retrieval-Based Approach to Table-Based Question Answering. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_50
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