Question Answering by Reasoning Across Documents with Graph Convolutional Networks

Nicola De Cao, Wilker Aziz, Ivan Titov


Abstract
Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentions of entities are nodes of this graph while edges encode relations between different mentions (e.g., within- and cross-document co-reference). Graph convolutional networks (GCNs) are applied to these graphs and trained to perform multi-step reasoning. Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset, WikiHop (Welbl et al., 2018).
Anthology ID:
N19-1240
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2306–2317
Language:
URL:
https://aclanthology.org/N19-1240
DOI:
10.18653/v1/N19-1240
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/N19-1240.pdf
Code
 worksheets/0xd2fb12d9