Question Answering by Reasoning Across Documents with Graph Convolutional Networks

Thursday, September 26, 2019, 11:00 am - 12:00 pm PSTiCal
CR# 689
This event is open to the public.
NL Seminar
Nicola De Cao
Video Recording:

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, WikiHo.

Nicola is a first year Ph.D. candidate at the Institute for Logic, Language and Computation ILLC at the University of Amsterdam. He is appointed at the School of Informatics at the University of Edinburgh supervised by Prof. Ivan Titov, and he is part of the EdinburghNLP group. Nicola’s work focuses on unstructured Machine Reading Comprehension also know as Question Answering.

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