# Abductive Matching in Question Answering

Source: [Abductive Matching in Question Answering](<https://www.sota2.com/research/paper/68fa63a61e209131cea0014d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:46:17.527Z

Authors: Kedar Dhamdhere, Kevin S. McCurley, Mukund Sundararajan, Ankur Taly

Published: 2017-09-10T03:42:43.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1709.03036>)

[PDF](<https://arxiv.org/pdf/1709.03036.pdf>)