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ImageTTR: Grounding Type Theory with Records in Image Classification for Visual Question Answering

Conference paper
Authors Arild Matsson
Simon Dobnik
Staffan Larsson
Published in Proceedings of the IWCS 2019 Workshop on Computing Semantics with Types, Frames and Related Structures, May 24, 2019, Gothenburg, Sweden / Rainer Osswald, Christian Retoré, Peter Sutton (Editors)
ISBN 978-1-950737-25-3
Publisher Association for Computational Linguistics
Place of publication Stroudsburg, PA
Publication year 2019
Published at Department of Swedish
Department of Philosophy, Linguistics and Theory of Science
Language en
Keywords Type Theory with Records, image classification, knowledge representation, visual question answering, computational semantics
Subject categories Language Technology (Computational Linguistics)


We present ImageTTR, an extension to the Python implementation of Type Theory with Records (pyTTR) which connects formal record type representation with image classifiers implemented as deep neural networks. The Type Theory with Records framework serves as a knowledge representation system for natural language the representations of which are grounded in perceptual information of neural networks. We demonstrate the benefits of this symbolic and data-driven hybrid approach on the task of visual question answering.

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