Summary of Few-shot Joint Multimodal Entity-relation Extraction Via Knowledge-enhanced Cross-modal Prompt Model, by Li Yuan et al.
Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model
by Li Yuan, Yi Cai, Junsheng Huang
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach for Joint Multimodal Entity-Relation Extraction (JMERE), which aims to extract entities and their relations from text-image pairs in social media posts. The proposed method, called Knowledge-Enhanced Cross-modal Prompt Model (KECPM), addresses the challenges of insufficient information in few-shot settings by guiding large language models to generate supplementary background knowledge. KECPM consists of two stages: knowledge ingestion, where prompts are formulated based on semantic similarity and refined through self-reflection, and a knowledge-enhanced language model stage that merges auxiliary knowledge with original input using a transformer-based model. The approach is evaluated on a few-shot dataset derived from the JMERE dataset, showing superiority over strong baselines in terms of micro and macro F_1 scores. The paper also provides qualitative analyses and case studies to demonstrate the effectiveness of KECPM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand what’s going on in social media posts by looking at both the text and images together. This is called Joint Multimodal Entity-Relation Extraction, or JMERE for short. It’s a difficult task because it requires lots of labeled data, which is hard to get. To solve this problem, researchers created a new method that helps large language models generate more information based on what they already know. This approach, called KECPM, can be used in situations where there isn’t much information available. The researchers tested their approach and found it worked better than other methods at extracting the right information from social media posts. |
Keywords
» Artificial intelligence » Few shot » Language model » Prompt » Transformer
