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        <title>Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets</title>
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    <abstract>Multimodal machine learning algorithms aim to learn visual-textual correspondences. Previous work suggests that concepts with concrete visual manifestations may be easier to learn than concepts with abstract ones. We give an algorithm for automatically computing the visual concreteness of words and topics within multimodal datasets. We apply the approach in four settings, ranging from image captions to images/text scraped from historical books. In addition to enabling explorations of concepts in multimodal datasets, our concreteness scores predict the capacity of machine learning algorithms to learn textual/visual relationships. We find that 1) concrete concepts are indeed easier to learn; 2) the large number of algorithms we consider have similar failure cases; 3) the precise positive relationship between concreteness and performance varies between datasets. We conclude with recommendations for using concreteness scores to facilitate future multimodal research.</abstract>
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    <identifier type="doi">10.18653/v1/N18-1199</identifier>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets
%A Hessel, Jack
%A Mimno, David
%A Lee, Lillian
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F hessel-etal-2018-quantifying
%X Multimodal machine learning algorithms aim to learn visual-textual correspondences. Previous work suggests that concepts with concrete visual manifestations may be easier to learn than concepts with abstract ones. We give an algorithm for automatically computing the visual concreteness of words and topics within multimodal datasets. We apply the approach in four settings, ranging from image captions to images/text scraped from historical books. In addition to enabling explorations of concepts in multimodal datasets, our concreteness scores predict the capacity of machine learning algorithms to learn textual/visual relationships. We find that 1) concrete concepts are indeed easier to learn; 2) the large number of algorithms we consider have similar failure cases; 3) the precise positive relationship between concreteness and performance varies between datasets. We conclude with recommendations for using concreteness scores to facilitate future multimodal research.
%R 10.18653/v1/N18-1199
%U https://aclanthology.org/N18-1199
%U https://doi.org/10.18653/v1/N18-1199
%P 2194-2205

Markdown (Informal)

[Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets](https://aclanthology.org/N18-1199) (Hessel et al., NAACL 2018)

• Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets (Hessel et al., NAACL 2018)

ACL

• Jack Hessel, David Mimno, and Lillian Lee. 2018. Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2194–2205, New Orleans, Louisiana. Association for Computational Linguistics.