Question Answering by Reasoning Across Documents with Graph Convolutional Networks [article]

Nicola De Cao, Wilker Aziz, Ivan Titov
2019 arXiv   pre-print
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)
more » ... 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).
arXiv:1808.09920v3 fatcat:pdd4mc5tkjgezeduqouky2yrba