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        <title>Semi-supervised multimodal coreference resolution in image narrations</title>
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        <namePart type="given">Arushi</namePart>
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    <name type="personal">
        <namePart type="given">Hakan</namePart>
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            <namePart type="given">Juan</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>In this paper, we study multimodal coreference resolution, specifically where a longer descriptive text, i.e., a narration is paired with an image. This poses significant challenges due to fine-grained image-text alignment, inherent ambiguity present in narrative language, and unavailability of large annotated training sets. To tackle these challenges, we present a data efficient semi-supervised approach that utilizes image-narration pairs to resolve coreferences and narrative grounding in a multimodal context. Our approach incorporates losses for both labeled and unlabeled data within a cross-modal framework. Our evaluation shows that the proposed approach outperforms strong baselines both quantitatively and qualitatively, for the tasks of coreference resolution and narrative grounding.</abstract>
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%0 Conference Proceedings
%T Semi-supervised multimodal coreference resolution in image narrations
%A Goel, Arushi
%A Fernando, Basura
%A Keller, Frank
%A Bilen, Hakan
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F goel-etal-2023-semi
%X In this paper, we study multimodal coreference resolution, specifically where a longer descriptive text, i.e., a narration is paired with an image. This poses significant challenges due to fine-grained image-text alignment, inherent ambiguity present in narrative language, and unavailability of large annotated training sets. To tackle these challenges, we present a data efficient semi-supervised approach that utilizes image-narration pairs to resolve coreferences and narrative grounding in a multimodal context. Our approach incorporates losses for both labeled and unlabeled data within a cross-modal framework. Our evaluation shows that the proposed approach outperforms strong baselines both quantitatively and qualitatively, for the tasks of coreference resolution and narrative grounding.
%R 10.18653/v1/2023.emnlp-main.682
%U https://aclanthology.org/2023.emnlp-main.682
%U https://doi.org/10.18653/v1/2023.emnlp-main.682
%P 11067-11081

Markdown (Informal)

[Semi-supervised multimodal coreference resolution in image narrations](https://aclanthology.org/2023.emnlp-main.682) (Goel et al., EMNLP 2023)

• Semi-supervised multimodal coreference resolution in image narrations (Goel et al., EMNLP 2023)

ACL

• Arushi Goel, Basura Fernando, Frank Keller, and Hakan Bilen. 2023. Semi-supervised multimodal coreference resolution in image narrations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11067–11081, Singapore. Association for Computational Linguistics.