Papers with sentence-level embedding

2 papers
Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model (2024.emnlp-industry)

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Challenge: Existing approaches to named entity recognition are domain specific and require a domain specific architecture.
Approach: They propose a retrieval augmented large language model for Named Entity Recognition . the model uses word-embedding over sentence-level embedding to fine tune .
Outcome: The proposed model outperforms existing models on the CrossNER dataset.
VICTR: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks (2020.coling-main)

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Challenge: Existing text-to-image generation models focus on generating high resolution images and neglect understanding text descriptions.
Approach: They propose a visual contextual text representation which captures rich visual semantic information of objects from text input.
Outcome: The proposed visual contextual text representation improves on the state-of-the-art models.

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