Abductive Matching in Question Answering

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, th...

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Bibliographic Details
Published inarXiv.org
Main Authors Dhamdhere, Kedar, McCurley, Kevin S, Sundararajan, Mukund, Taly, Ankur
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 10.09.2017
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Artificial intelligence
Machine learning
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Summary: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.
ISSN:2331-8422