Semi-supervised multimodal coreference resolution in image narrations

Autor:	Goel, Arushi, Fernando, Basura, Keller, Frank, Bilen, Hakan
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Long paper at EMNLP'23-Main
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.13619 Zobrazit plný text záznamu View this record from Arxiv