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arXiv · 1709.03036

Abductive Matching in Question Answering

Kedar Dhamdhere·Kevin S. McCurley·Mukund Sundararajan·Ankur Taly
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Abstract

We study question-answering over semi-structured data. We introduce a new way to apply the technique of semantic parsing by applying machine learning only to provide annotations that the system infers to be missing; all the other parsing logic is in the form of manually authored rules. In effect, the machine learning is used to provide non-syntactic matches, a step that is ill-suited to manual rules. The advantage of this approach is in its debuggability and in its transparency to the end-user. We demonstrate the effectiveness of the approach by achieving state-of-the-art performance of 40.42% accuracy on a standard benchmark dataset over tables from Wikipedia.

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Kedar Dhamdhere, Kevin S. McCurley, Mukund Sundararajan, Ankur Taly. 2017-09-10. Abductive Matching in Question Answering. https://arxiv.org/abs/1709.03036

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