Challenge: Entity linking is a fundamental task in Natural Language Processing (NLP), connecting mentions within unstructured contexts to their corresponding entities in a Knowledge Base (KB).
Approach: They propose a dual-encoder framework that can efficiently match mentions to two-encoding frameworks by a global-view.
Outcome: The proposed framework achieves state-of-the-art on several entity linking benchmarks.

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Challenge: Pre-trained big models have delivered top performance in Seq2seq modeling, but their deployments in real-world applications are often hindered by excessive computations and memory demands.
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MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations (2021.emnlp-main)

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Challenge: Recent advances in entity retrieval ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions.
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Scalable Zero-shot Entity Linking with Dense Entity Retrieval (2020.emnlp-main)

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Challenge: Existing methods for entity linking use manually curated mention tables and incoming Wikipedia link popularity.
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Contextual Augmentation for Entity Linking using Large Language Models (2025.coling-main)

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Challenge: Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph.
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Ameli: Enhancing Multimodal Entity Linking with Fine-Grained Attributes (2024.eacl-long)

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Challenge: Experimental results show that understanding attributes of mentions from text descriptions and visual images plays a vital role in multimodal entity linking.
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Multi-Granularity Structural Knowledge Distillation for Language Model Compression (2022.acl-long)

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Challenge: Existing methods to transfer knowledge to a small model are not enough to represent the rich semantics of a text.
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3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding (2024.findings-acl)

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Challenge: Existing models for visually rich document understanding do not account for the diverse carriers of document versions and their associated noises.
Approach: They propose a multimodal, multi-task, multiteacher joint-grained knowledge distillation model for visually-rich form document understanding.
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ThinkLinker: From Low-Rank Interaction to Knowledge-Aware Verification for Multimodal Entity Linking (2026.findings-acl)

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Challenge: Existing methods for multimodal entity linking rely on textual context for disambiguation . textual contextual information alone fails to resolve ambiguity, leading to unreliable disambiguations in weak contexts.
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Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization (2024.findings-acl)

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Challenge: Multimodal Summarization with Multimodal Output (MSMO) is a new approach to produce a multimodal summary that integrates both text and relevant images.
Approach: They propose an Entity-Guided Multimodal Summarization model that integrates both text and relevant images to produce a multimodal summary.
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Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)

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Challenge: Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks.
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