Papers with few-shot relation extraction methods

1 papers
Few-Shot Relation Extraction with Hybrid Visual Evidence (2024.lrec-main)

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Challenge: Existing few-shot relation extraction methods focus on uni-modal information such as text only. Existing methods focus only on text, requiring only a few labeled instances for training.
Approach: They propose a multi-modal few-shot relation extraction model that leverages both textual and visual semantic information to learn a multiple-modal representation jointly.
Outcome: The proposed model leverages both textual and visual semantic information to learn a multi-modal representation jointly.

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