Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-to-end deep neural networks. Specialized neural models have been developed for extracting answers from either text alone or Knowledge Bases (KBs) alone. In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus. Building on recent advances in graph representation learning we propose a novel model, GRAFT-Net, for extracting answers from a question-specific subgraph containing text and KB entities and relations. We construct a suite of benchmark tasks for this problem, varying the difficulty of questions, the amount of training data, and KB completeness. We show that GRAFT-Net is competitive with the state-of-the-art when tested using either KBs or text alone, and vastly outperforms existing methods in the combined setting. Source code is available at https://github.com/OceanskySun/GraftNet .
@article{arxiv.1809.00782,
title = {Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text},
author = {Haitian Sun and Bhuwan Dhingra and Manzil Zaheer and Kathryn Mazaitis and Ruslan Salakhutdinov and William W. Cohen},
journal= {arXiv preprint arXiv:1809.00782},
year = {2018}
}