Semi-supervised multimodal coreference resolution in image narrations
Arushi Goel, Basura Fernando, Frank Keller, Hakan Bilen
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.
BibTeX
@inproceedings{
goel2023semisupervised,
title={Semi-supervised multimodal coreference resolution in image narrations},
author={Arushi Goel and Basura Fernando and Frank Keller and Hakan Bilen},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=K35sqjeg5J}
}