Papers by Danae Sanchez Villegas

3 papers
Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks (2024.findings-eacl)

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Challenge: Prior work on multimodal content classification has not addressed these challenges.
Approach: They propose to use two auxiliary tasks to fine-tune multimodal models to address hidden cross-modal semantics and weak image-text relationships when modeling text and images.
Outcome: The proposed model improves by up to 2.6 F1 score across five diverse social media datasets.
Combining Humor and Sarcasm for Improving Political Parody Detection (2022.naacl-main)

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Challenge: Parody is a figurative device used for mimicking entities for comedic or critical purposes.
Approach: They propose a multi-encoder model that combines three parallel encoders to enrich parody-specific representations with humor and sarcasm information.
Outcome: The proposed model outperforms state-of-the-art methods on a dataset of political parody tweets.
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users (2025.acl-long)

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Challenge: Despite high adoption rate of Large Language Models, there are limitations related to contextual understanding, cultural sensitivity, and complex scene understanding.
Approach: They conduct a user survey to identify adoption patterns and key challenges users face with such technologies.
Outcome: The proposed models have high adoption rates but still face limitations in visual aids.

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