University of Cambridge > Talks.cam > NLIP Seminar Series > Graph Neural Networks for Knowledge Base Question Answering

Graph Neural Networks for Knowledge Base Question Answering

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Andrew Caines.

Room changed

In this talk, we present a semantic parsing approach to Knowledge Base Question Answering. We address the problem of learning vector representations for complex semantic parses that consist of multiple entities and relations. Previous work largely focused on selecting the correct semantic relations for a question and disregarded the structure of the semantic parse: the connections between entities and the directions of the relations. We propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse.

We will present a formulation of Gated Graph Neural Networks for labeled knowledge base subgraphs and show how it can be used in a question answering pipeline. Empirically, we demonstrate on two data sets that the graph networks outperform the baseline models that do not explicitly model the semantic structure.

This talk is part of the NLIP Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2020 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity