@inproceedings{diwan-etal-2022-winoground,
title = "Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality",
author = "Diwan, Anuj and
Berry, Layne and
Choi, Eunsol and
Harwath, David and
Mahowald, Kyle",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.143",
doi = "10.18653/v1/2022.emnlp-main.143",
pages = "2236--2250",
abstract = "Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning. Yet, they fail miserably on the recently proposed Winoground dataset, which challenges models to match paired images and English captions, with items constructed to overlap lexically but differ in meaning (e.g., {``}there is a mug in some grass{''} vs. {``}there is some grass in a mug{''}). By annotating the dataset using new fine-grained tags, we show that solving the Winoground task requires not just compositional language understanding, but a host of other abilities like commonsense reasoning or locating small, out-of-focus objects in low-resolution images. In this paper, we identify the dataset{'}s main challenges through a suite of experiments on related tasks (probing task, image retrieval task), data augmentation, and manual inspection of the dataset. Our analysis suggests that a main challenge in visuolinguistic models may lie in fusing visual and textual representations, rather than in compositional language understanding. We release our annotation and code at \url{https://github.com/ajd12342/why-winoground-hard}.",
}
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<abstract>Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning. Yet, they fail miserably on the recently proposed Winoground dataset, which challenges models to match paired images and English captions, with items constructed to overlap lexically but differ in meaning (e.g., “there is a mug in some grass” vs. “there is some grass in a mug”). By annotating the dataset using new fine-grained tags, we show that solving the Winoground task requires not just compositional language understanding, but a host of other abilities like commonsense reasoning or locating small, out-of-focus objects in low-resolution images. In this paper, we identify the dataset’s main challenges through a suite of experiments on related tasks (probing task, image retrieval task), data augmentation, and manual inspection of the dataset. Our analysis suggests that a main challenge in visuolinguistic models may lie in fusing visual and textual representations, rather than in compositional language understanding. We release our annotation and code at https://github.com/ajd12342/why-winoground-hard.</abstract>
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%0 Conference Proceedings
%T Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality
%A Diwan, Anuj
%A Berry, Layne
%A Choi, Eunsol
%A Harwath, David
%A Mahowald, Kyle
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F diwan-etal-2022-winoground
%X Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning. Yet, they fail miserably on the recently proposed Winoground dataset, which challenges models to match paired images and English captions, with items constructed to overlap lexically but differ in meaning (e.g., “there is a mug in some grass” vs. “there is some grass in a mug”). By annotating the dataset using new fine-grained tags, we show that solving the Winoground task requires not just compositional language understanding, but a host of other abilities like commonsense reasoning or locating small, out-of-focus objects in low-resolution images. In this paper, we identify the dataset’s main challenges through a suite of experiments on related tasks (probing task, image retrieval task), data augmentation, and manual inspection of the dataset. Our analysis suggests that a main challenge in visuolinguistic models may lie in fusing visual and textual representations, rather than in compositional language understanding. We release our annotation and code at https://github.com/ajd12342/why-winoground-hard.
%R 10.18653/v1/2022.emnlp-main.143
%U https://aclanthology.org/2022.emnlp-main.143
%U https://doi.org/10.18653/v1/2022.emnlp-main.143
%P 2236-2250
Markdown (Informal)
[Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality](https://aclanthology.org/2022.emnlp-main.143) (Diwan et al., EMNLP 2022)
ACL