Papers by Julia Kruk

3 papers
Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts (D19-1)

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Challenge: Existing studies on text-image content have focused on image as primary content, and text as secondary content.
Approach: They propose a multimodal dataset of 1299 Instagram posts labeled for three orthogonal taxonomies . they show that employing both text and image improves intent detection by 9.6 .
Outcome: The proposed model shows that using both text and image improves intent detection by 9.6 compared to using only the image modality.
Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles (2024.acl-long)

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Challenge: a dog whistle is a coded communication that carries a secondary meaning to specific audiences and is often weaponized for racial and socioeconomic discrimination.
Approach: They propose an approach for word-sense disambiguation of dog whistles from standard speech using Large Language Models.
Outcome: The proposed method allows disambiguation of dog whistles from standard speech using large language models.
Impressions: Visual Semiotics and Aesthetic Impact Understanding (2023.emnlp-main)

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Challenge: Existing image captioning and conditional generation models struggle to simulate plausible human responses to images.
Approach: They propose a dataset to investigate the semiotics of images and how visual features and design choices can elicit specific emotions, thoughts and beliefs.
Outcome: The proposed dataset improves existing models for image captioning and conditional generation.

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