Query-Reduction Networks for Question Answering
Abstract
In this paper, we study the problem of question answering when reasoning over multiple facts is required. We propose Query-Reduction Network (QRN), a variant of Recurrent Neural Network (RNN) that effectively handles both short-term (local) and long-term (global) sequential dependencies to reason over multiple facts. QRN considers the context sentences as a sequence of state-changing triggers, and reduces the original query to a more informed query as it observes each trigger (context sentence) through time. Our experiments show that QRN produces the state-of-the-art results in bAbI QA and dialog tasks, and in a real goal-oriented dialog dataset. In addition, QRN formulation allows parallelization on RNN's time axis, saving an order of magnitude in time complexity for training and inference.
Cite
@article{arxiv.1606.04582,
title = {Query-Reduction Networks for Question Answering},
author = {Minjoon Seo and Sewon Min and Ali Farhadi and Hannaneh Hajishirzi},
journal= {arXiv preprint arXiv:1606.04582},
year = {2017}
}
Comments
Published as a conference paper at ICLR 2017. Title of the paper has changed from "Query-Regression Networks for Machine Comprehension"