# Named Entity and Relation Extraction with Multi-Modal Retrieval

arXiv: https://arxiv.org/abs/2212.01612 · HTML: https://mlsanity.com/paper/2212.01612/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 54 references that are in the map.
- First posted on arXiv 3 Dec 2022.

## Builds on (most cited first, 29 of 29)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [A Corpus for Reasoning About Natural Language Grounded in Photographs](https://mlsanity.com/paper/1811.00491/) (2018)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections](https://mlsanity.com/paper/2205.12005/) (2022)
- [E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning](https://mlsanity.com/paper/2106.01804/) (2021)
- [Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://mlsanity.com/paper/2105.03654/) (2021)
- [Guiding Neural Machine Translation with Retrieved Translation Pieces](https://mlsanity.com/paper/1804.02559/) (2018)
- [Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data](https://mlsanity.com/paper/2203.08773/) (2022)
- [Multimodal Named Entity Recognition for Short Social Media Posts](https://mlsanity.com/paper/1802.07862/) (2018)
- [Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention](https://mlsanity.com/paper/2112.03254/) (2021)
- [RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER](https://mlsanity.com/paper/2102.02967/) (2021)
- [EntQA: Entity Linking as Question Answering](https://mlsanity.com/paper/2110.02369/) (2021)
- [WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types](https://mlsanity.com/paper/2204.06347/) (2022)
- [ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition](https://mlsanity.com/paper/2112.06482/) (2021)
- [DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition](https://mlsanity.com/paper/2203.00545/) (2022)
- [Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings](https://mlsanity.com/paper/2011.01565/) (2020)
- [Domain-Specific NER via Retrieving Correlated Samples](https://mlsanity.com/paper/2208.12995/) (2022)
- [DAMO-NLP at NLPCC-2022 Task 2: Knowledge Enhanced Robust NER for Speech Entity Linking](https://mlsanity.com/paper/2209.13187/) (2022)

## Built on by (most cited first, 26 of 26)

- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [UMIE: Unified Multimodal Information Extraction with Instruction Tuning](https://mlsanity.com/paper/2401.03082/) (2024)
- [Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge](https://mlsanity.com/paper/2305.12212/) (2023)
- [DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition](https://mlsanity.com/paper/2305.03688/) (2023)
- [Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis](https://mlsanity.com/paper/2305.16166/) (2023)
- [Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking](https://mlsanity.com/paper/2306.12245/) (2023)
- [LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition](https://mlsanity.com/paper/2402.09989/) (2024)
- [Collaborative Multi-LoRA Experts with Achievement-based Multi-Tasks Loss for Unified Multimodal Information Extraction](https://mlsanity.com/paper/2505.06303/) (2025)
- [Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction](https://mlsanity.com/paper/2310.16822/) (2023)
- [SCANNER: Knowledge-Enhanced Approach for Robust Multi-modal Named Entity Recognition of Unseen Entities](https://mlsanity.com/paper/2404.01914/) (2024)
- [Integrating Large Pre-trained Models into Multimodal Named Entity Recognition with Evidential Fusion](https://mlsanity.com/paper/2306.16991/) (2023)
- [Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition](https://mlsanity.com/paper/2407.21033/) (2024)
- [Retrieval-Augmented Meta Learning for Low-Resource Text Classification](https://mlsanity.com/paper/2309.04979/) (2023)
- [Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction](https://mlsanity.com/paper/2312.03025/) (2023)
- [Shapley Value-based Contrastive Alignment for Multimodal Information Extraction](https://mlsanity.com/paper/2407.17854/) (2024)
- [Advancing Grounded Multimodal Named Entity Recognition via LLM-Based Reformulation and Box-Based Segmentation](https://mlsanity.com/paper/2406.07268/) (2024)
- [Learning Robust Named Entity Recognizers From Noisy Data With Retrieval Augmentation](https://mlsanity.com/paper/2407.18562/) (2024)
- [Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction](https://mlsanity.com/paper/2502.12614/) (2025)
- [ReFineG: Synergizing Small Supervised Models and LLMs for Low-Resource Grounded Multimodal NER](https://mlsanity.com/paper/2509.10975/) (2025)
- [SAKE: Self-aware Knowledge Exploitation-Exploration for Grounded Multimodal Named Entity Recognition](https://mlsanity.com/paper/2604.20146/) (2026)
- [NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension](https://mlsanity.com/paper/2305.03970/) (2023)
- [DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem](https://mlsanity.com/paper/2402.16159/) (2024)
- [A Proposal-Free Query-Guided Network for Grounded Multimodal Named Entity Recognition](https://mlsanity.com/paper/2603.17314/) (2026)
- [Code-MIE: A Code-style Model for Multimodal Information Extraction with Scene Graph and Entity Attribute Knowledge Enhancement](https://mlsanity.com/paper/2603.20781/) (2026)
- [SAVER: Selective As-Needed Vision Evidence for Multimodal Information Extraction](https://mlsanity.com/paper/2605.20713/) (2026)
- [SAMA: Semantic Anchor-aligned Augmentation for Unified Low-Resource Multimodal Information Extraction](https://mlsanity.com/paper/2606.18780/) (2026)

## Abstract (arXiv)

Multi-modal named entity recognition (NER) and relation extraction (RE) aim to leverage relevant image information to improve the performance of NER and RE. Most existing efforts largely focused on directly extracting potentially useful information from images (such as pixel-level features, identified objects, and associated captions). However, such extraction processes may not be knowledge aware, resulting in information that may not be highly relevant. In this paper, we propose a novel Multi-modal Retrieval based framework (MoRe). MoRe contains a text retrieval module and an image-based retrieval module, which retrieve related knowledge of the input text and image in the knowledge corpus respectively. Next, the retrieval results are sent to the textual and visual models respectively for predictions. Finally, a Mixture of Experts (MoE) module combines the predictions from the two models to make the final decision. Our experiments show that both our textual model and visual model can achieve state-of-the-art performance on four multi-modal NER datasets and one multi-modal RE dataset. With MoE, the model performance can be further improved and our analysis demonstrates the benefits of integrating both textual and visual cues for such tasks.