Question Answering by Reasoning Across Documents with Graph Convolutional Networks

Nicola De Cao; Wilker Aziz; Ivan Titov

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arXiv · 1808.09920

Question Answering by Reasoning Across Documents with Graph Convolutional Networks

Nicola De Cao·Wilker Aziz·Ivan Titov

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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).

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Nicola De Cao, Wilker Aziz, Ivan Titov. 2018-08-29. Question Answering by Reasoning Across Documents with Graph Convolutional Networks. https://arxiv.org/abs/1808.09920

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