# A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction arXiv: https://arxiv.org/abs/2101.10213 ยท HTML: https://mlsanity.com/paper/2101.10213/ ## Key facts - Cited by 26 papers in the map (data through 14 Oct 2026). - Builds on 17 of its 43 references that are in the map. - First posted on arXiv 25 Jan 2021. ## Builds on (most cited first, 17 of 17) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017) - [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019) - [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018) - [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019) - [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016) - [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019) - [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017) - [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019) - [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019) - [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018) - [More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction](https://mlsanity.com/paper/2004.03186/) (2020) - [A Hierarchical Framework for Relation Extraction with Reinforcement Learning](https://mlsanity.com/paper/1811.03925/) (2018) - [TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition](https://mlsanity.com/paper/2004.07493/) (2020) - [Adversarial training for multi-context joint entity and relation extraction](https://mlsanity.com/paper/1808.06876/) (2018) - [NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction](https://mlsanity.com/paper/1909.02177/) (2019) - [Learning from Explanations with Neural Execution Tree](https://mlsanity.com/paper/1911.01352/) (2019) ## Built on by (most cited first, 26 of 26) - [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021) - [Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers](https://mlsanity.com/paper/2107.12636/) (2021) - [Packed Levitated Marker for Entity and Relation Extraction](https://mlsanity.com/paper/2109.06067/) (2021) - [A Partition Filter Network for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2108.12202/) (2021) - [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023) - [Parallel Instance Query Network for Named Entity Recognition](https://mlsanity.com/paper/2203.10545/) (2022) - [KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports](https://mlsanity.com/paper/2208.02140/) (2022) - [PromptNER: Prompt Locating and Typing for Named Entity Recognition](https://mlsanity.com/paper/2305.17104/) (2023) - [Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem](https://mlsanity.com/paper/2210.11694/) (2022) - [LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty](https://mlsanity.com/paper/2402.10573/) (2024) - [Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks](https://mlsanity.com/paper/2310.17238/) (2023) - [R1-RE: Cross-Domain Relation Extraction with RLVR](https://mlsanity.com/paper/2507.04642/) (2025) - [KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents](https://mlsanity.com/paper/2210.09163/) (2022) - [Query-based Instance Discrimination Network for Relational Triple Extraction](https://mlsanity.com/paper/2211.01797/) (2022) - [DiffusionNER: Boundary Diffusion for Named Entity Recognition](https://mlsanity.com/paper/2305.13298/) (2023) - [MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition](https://mlsanity.com/paper/2310.08298/) (2023) - [MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries](https://mlsanity.com/paper/2408.12980/) (2024) - [A New Entity Extraction Method Based on Machine Reading Comprehension](https://mlsanity.com/paper/2108.06444/) (2021) - [PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extraction](https://mlsanity.com/paper/2405.20787/) (2024) - [Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition](https://mlsanity.com/paper/2207.03300/) (2022) - [Span-based joint entity and relation extraction augmented with sequence tagging mechanism](https://mlsanity.com/paper/2210.12720/) (2022) - [Towards automating Numerical Consistency Checks in Financial Reports](https://mlsanity.com/paper/2211.06112/) (2022) - [Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction](https://mlsanity.com/paper/2401.10189/) (2024) - [IPED: An Implicit Perspective for Relational Triple Extraction based on Diffusion Model](https://mlsanity.com/paper/2403.00808/) (2024) - [Similarity-based Memory Enhanced Joint Entity and Relation Extraction](https://mlsanity.com/paper/2307.11762/) (2023) - [Hallucination-Resistant Relation Extraction via Dependency-Aware Sentence Simplification and Two-tiered Hierarchical Refinement](https://mlsanity.com/paper/2508.14391/) (2025) ## Abstract (arXiv) Joint entity and relation extraction framework constructs a unified model to perform entity recognition and relation extraction simultaneously, which can exploit the dependency between the two tasks to mitigate the error propagation problem suffered by the pipeline model. Current efforts on joint entity and relation extraction focus on enhancing the interaction between entity recognition and relation extraction through parameter sharing, joint decoding, or other ad-hoc tricks (e.g., modeled as a semi-Markov decision process, cast as a multi-round reading comprehension task). However, there are still two issues on the table. First, the interaction utilized by most methods is still weak and uni-directional, which is unable to model the mutual dependency between the two tasks. Second, relation triggers are ignored by most methods, which can help explain why humans would extract a relation in the sentence. They're essential for relation extraction but overlooked. To this end, we present a Trigger-Sense Memory Flow Framework (TriMF) for joint entity and relation extraction. We build a memory module to remember category representations learned in entity recognition and relation extraction tasks. And based on it, we design a multi-level memory flow attention mechanism to enhance the bi-directional interaction between entity recognition and relation extraction. Moreover, without any human annotations, our model can enhance relation trigger information in a sentence through a trigger sensor module, which improves the model performance and makes model predictions with better interpretation. Experiment results show that our proposed framework achieves state-of-the-art results by improves the relation F1 to 52.44% (+3.2%) on SciERC, 66.49% (+4.9%) on ACE05, 72.35% (+0.6%) on CoNLL04 and 80.66% (+2.3%) on ADE.