A Neural Question Answering Model Based on Semi-Structured Tables
作者： Houfeng Wang , Xiaodong Zhang , Shuming Ma , Hao Wang , Xu Sun
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摘要: Most question answering (QA) systems are based on raw text and structured knowledge graph. However, corpora hard for QA system to understand, graph needs intensive manual work, while it is relatively easy obtain semi-structured tables from many sources directly, or build them automatically. In this paper, we an end-to-end answer multiple choice questions with as its knowledge. Our answers queries by two steps. First, finds the most similar tables. Then measures relevance between each candidate table cells, choose related cell source of answer. The evaluated TabMCQ dataset, gets a huge improvement compared state art.
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