Abductive Matching in Question Answering
Computation and Language
2017-09-12 v1 Machine Learning
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.
Cite
@article{arxiv.1709.03036,
title = {Abductive Matching in Question Answering},
author = {Kedar Dhamdhere and Kevin S. McCurley and Mukund Sundararajan and Ankur Taly},
journal= {arXiv preprint arXiv:1709.03036},
year = {2017}
}