Papers with text-based approach

4 papers
CRAPES:Cross-modal Annotation Projection for Visual Semantic Role Labeling (2023.starsem-1)

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Challenge: Existing approaches to image comprehension limit the image to a single action, while text-based approaches label all actions in a sentence.
Approach: They propose to expand GSR to follow more liberal text-based approach to action and participant identification.
Outcome: The proposed approach improves image comprehension on a SWiG dataset by 28.6 points.
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation (N19-1)

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Challenge: Text generation with generative adversarial networks (GANs) can be divided into text-based and code-based categories depending on the type of signals used for discrimination.
Approach: They propose a text-based approach to exploit generative adversarial networks (GANs) by using autoencoders to provide a continuous representation of sentences, which they will refer to as soft-text, and hybrid latent code and text-oriented approaches with one or more discriminators.
Outcome: The proposed approach outperforms the traditional GAN-based methods on two well-known datasets.
Form2Seq : A Framework for Higher-Order Form Structure Extraction (2020.emnlp-main)

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Challenge: Document structure extraction is a widely researched area for decades due to image resolution and poor semantics.
Approach: They propose a sequence-to-sequence framework for document structure extraction using text . they use a text-based framework to classify low-level constituent elements into ten types .
Outcome: The proposed framework outperforms existing methods for document structure extraction on ICDAR 2013 dataset.
A Couch Potato is not a Potato on a Couch: Prompting Strategies, Image Generation, and Compositionality Prediction for Noun Compounds (2025.findings-acl)

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Challenge: a new method to predict the compositionality of English noun compounds is proposed .
Approach: They propose a visual modality and vision transformers to predict the compositionality of English noun compounds.
Outcome: The proposed method compared with a state-of-the-art text-based approach reveals complementary contributions regarding features and degrees of abstractness in English noun compounds.

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