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Learning to Compose Spatial Relations with Grounded Neural Language Models

Conference paper
Authors Mehdi Ghanimifard
Simon Dobnik
Published in Proceedings of IWCS 2017: 12th International Conference on Computational Semantics, Montpellier 19-22 September 2017 / Claire Gardent and Christian Retoré (eds.)
Publisher Association for Computational Linguistics
Publication year 2017
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links aclweb.org/anthology/W17-6808
https://gup.ub.gu.se/file/207070
Keywords Symbol Grounding Grounded Language Model Language and Vision Recurrent Neural Networks Representation Learning Representation of Meaning
Subject categories Computational linguistics, Linguistics

Abstract

Language is compositional: we can generate and interpret novel sentences by having a notion of the meaning of their individual parts. Spatial descriptions are grounded in perceptional representations but their meaning is also defined by what neighbouring words they co-occur with. In this paper, we examine how language models conditioned on perceptual features can capture the semantics of composed phrases as well as of individual words. We generate a synthetic dataset of spatial descriptions referring to perceptual scenes and examine how grounded language models built with deep neural networks can account for compositionality of descriptions – by evaluating how the learned language models can deal with novel grounded composed descriptions and novel grounded decomposed descriptions, constituents previously not seen in isolation.

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