@inproceedings{ilharco-etal-2021-probing,
title = "Probing Contextual Language Models for Common Ground with Visual Representations",
author = "Ilharco, Gabriel and
Zellers, Rowan and
Farhadi, Ali and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.422",
doi = "10.18653/v1/2021.naacl-main.422",
pages = "5367--5377",
abstract = "The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations. In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with corresponding visual representations? We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations. Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories. Moreover, they are effective in retrieving specific instances of image patches; textual context plays an important role in this process. Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans. We hope our analyses inspire future research in understanding and improving the visual capabilities of language models.",
}
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<abstract>The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations. In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with corresponding visual representations? We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations. Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories. Moreover, they are effective in retrieving specific instances of image patches; textual context plays an important role in this process. Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans. We hope our analyses inspire future research in understanding and improving the visual capabilities of language models.</abstract>
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%0 Conference Proceedings
%T Probing Contextual Language Models for Common Ground with Visual Representations
%A Ilharco, Gabriel
%A Zellers, Rowan
%A Farhadi, Ali
%A Hajishirzi, Hannaneh
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Online
%F ilharco-etal-2021-probing
%X The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations. In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with corresponding visual representations? We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations. Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories. Moreover, they are effective in retrieving specific instances of image patches; textual context plays an important role in this process. Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans. We hope our analyses inspire future research in understanding and improving the visual capabilities of language models.
%R 10.18653/v1/2021.naacl-main.422
%U https://aclanthology.org/2021.naacl-main.422
%U https://doi.org/10.18653/v1/2021.naacl-main.422
%P 5367-5377
Markdown (Informal)
[Probing Contextual Language Models for Common Ground with Visual Representations](https://aclanthology.org/2021.naacl-main.422) (Ilharco et al., NAACL 2021)
ACL