# Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking

arXiv: https://arxiv.org/abs/2306.12245 · HTML: https://mlsanity.com/paper/2306.12245/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 66 references that are in the map.
- First posted on arXiv 21 Jun 2023.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [REL: An Entity Linker Standing on the Shoulders of Giants](https://mlsanity.com/paper/2006.01969/) (2020)
- [Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking](https://mlsanity.com/paper/2003.05473/) (2020)
- [Fast and Accurate Entity Recognition with Iterated Dilated Convolutions](https://mlsanity.com/paper/1702.02098/) (2017)
- [EntQA: Entity Linking as Question Answering](https://mlsanity.com/paper/2110.02369/) (2021)
- [Joint Learning of Named Entity Recognition and Entity Linking](https://mlsanity.com/paper/1907.08243/) (2019)
- [Contrastive Learning with Hard Negative Entities for Entity Set Expansion](https://mlsanity.com/paper/2204.07789/) (2022)
- [Named Entity and Relation Extraction with Multi-Modal Retrieval](https://mlsanity.com/paper/2212.01612/) (2022)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [Highly Parallel Autoregressive Entity Linking with Discriminative Correction](https://mlsanity.com/paper/2109.03792/) (2021)
- [Automatic Context Pattern Generation for Entity Set Expansion](https://mlsanity.com/paper/2207.08087/) (2022)
- [A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization](https://mlsanity.com/paper/1812.06081/) (2018)
- [Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment](https://mlsanity.com/paper/2302.08774/) (2023)
- [TempEL: Linking Dynamically Evolving and Newly Emerging Entities](https://mlsanity.com/paper/2302.02500/) (2023)
- [Medical Entity Linking using Triplet Network](https://mlsanity.com/paper/2012.11164/) (2020)
- [Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking](https://mlsanity.com/paper/2202.13404/) (2022)
- [DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition](https://mlsanity.com/paper/2305.03688/) (2023)
- [DAMO-NLP at NLPCC-2022 Task 2: Knowledge Enhanced Robust NER for Speech Entity Linking](https://mlsanity.com/paper/2209.13187/) (2022)
- [Personal Entity, Concept, and Named Entity Linking in Conversations](https://mlsanity.com/paper/2206.07836/) (2022)
- [Entity Commonsense Representation for Neural Abstractive Summarization](https://mlsanity.com/paper/1806.05504/) (2018)
- [Learning Entity Linking Features for Emerging Entities](https://mlsanity.com/paper/2208.03877/) (2022)

## Built on by (most cited first, 7 of 7)

- [A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check](https://mlsanity.com/paper/2310.09119/) (2023)
- [Refine Knowledge of Large Language Models via Adaptive Contrastive Learning](https://mlsanity.com/paper/2502.07184/) (2025)
- [MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities](https://mlsanity.com/paper/2307.14878/) (2023)
- [Retrieval-Augmented Meta Learning for Low-Resource Text Classification](https://mlsanity.com/paper/2309.04979/) (2023)
- [UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities](https://mlsanity.com/paper/2403.04247/) (2024)
- [Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection](https://mlsanity.com/paper/2309.04971/) (2023)
- [An Anchor Learning Approach for Citation Field Learning](https://mlsanity.com/paper/2309.03559/) (2023)

## Abstract (arXiv)

Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs. The general form of EL (i.e., end-to-end EL) aims to first find mentions in the given input document and then link the mentions to corresponding entities in a specific knowledge base. Recently, the paradigm of retriever-reader promotes the progress of end-to-end EL, benefiting from the advantages of dense entity retrieval and machine reading comprehension. However, the existing study only trains the retriever and the reader separately in a pipeline manner, which ignores the benefit that the interaction between the retriever and the reader can bring to the task. To advance the retriever-reader paradigm to perform more perfectly on end-to-end EL, we propose BEER2, a Bidirectional End-to-End training framework for Retriever and Reader. Through our designed bidirectional end-to-end training, BEER2 guides the retriever and the reader to learn from each other, make progress together, and ultimately improve EL performance. Extensive experiments on benchmarks of multiple domains demonstrate the effectiveness of our proposed BEER2.