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        <title>Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents</title>
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            <namePart type="given">Xiaojun</namePart>
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    <abstract>Images and text co-occur constantly on the web, but explicit links between images and sentences (or other intra-document textual units) are often not present. We present algorithms that discover image-sentence relationships without relying on explicit multimodal annotation in training. We experiment on seven datasets of varying difficulty, ranging from documents consisting of groups of images captioned post hoc by crowdworkers to naturally-occurring user-generated multimodal documents. We find that a structured training objective based on identifying whether collections of images and sentences co-occur in documents can suffice to predict links between specific sentences and specific images within the same document at test time.</abstract>
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%0 Conference Proceedings
%T Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents
%A Hessel, Jack
%A Lee, Lillian
%A Mimno, David
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F hessel-etal-2019-unsupervised
%X Images and text co-occur constantly on the web, but explicit links between images and sentences (or other intra-document textual units) are often not present. We present algorithms that discover image-sentence relationships without relying on explicit multimodal annotation in training. We experiment on seven datasets of varying difficulty, ranging from documents consisting of groups of images captioned post hoc by crowdworkers to naturally-occurring user-generated multimodal documents. We find that a structured training objective based on identifying whether collections of images and sentences co-occur in documents can suffice to predict links between specific sentences and specific images within the same document at test time.
%R 10.18653/v1/D19-1210
%U https://aclanthology.org/D19-1210
%U https://doi.org/10.18653/v1/D19-1210
%P 2034-2045

Markdown (Informal)

[Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents](https://aclanthology.org/D19-1210) (Hessel et al., EMNLP-IJCNLP 2019)

• Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents (Hessel et al., EMNLP-IJCNLP 2019)

ACL

• Jack Hessel, Lillian Lee, and David Mimno. 2019. Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2034–2045, Hong Kong, China. Association for Computational Linguistics.