Papers with DocRED
Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion (2022.findings-acl)
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| Challenge: | Document-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document. |
| Approach: | They propose an evidence-enhanced framework that empowers document-level relation extraction (DocRE) Eider efficiently extracts evidence and effectively fuses extracted evidence in inference. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three benchmark datasets. |
DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)
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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
| Challenge: | Existing relation extraction methods focus on extracting intra-sentence relations for single entities. |
| Approach: | They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities . |
| Outcome: | The proposed dataset is the largest human-annotated dataset for document-level RE from plain text. |
MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction (2021.findings-acl)
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| Challenge: | Existing studies on document-level relation extraction focus on sentencelevel RE, but recent studies reveal that a large number of relations can actually be expressed through multiple sentences, which necessitates document- level RE. |
| Approach: | They propose a document-level relation extraction model that captures local and global contextual information as well as close and distant mention interactions. |
| Outcome: | The proposed model outperforms state-of-the-art models on three widely used datasets, namely DocRED, CDR, and GDA. |
Double Graph Based Reasoning for Document-level Relation Extraction (2020.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction fail to recognize relations between entities across sentences. |
| Approach: | They propose a method to recognize relations for long paragraphs by a Graph Aggregation-and-Inference Network (GAIN) they propose to use a heterogeneous mention-level graph and an entity-level EG graph to analyze the relationships. |
| Outcome: | The proposed method achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. |
Bootstrapping Relation Extractors using Syntactic Search by Examples (2021.eacl-main)
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| Challenge: | Existing methods for supervised relation extraction still require a large quantity of training data. |
| Approach: | They propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts. |
| Outcome: | The proposed method outperforms models trained on manual and distant data augmentation techniques and the search-based approach with the NLG method. |
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling (2022.findings-emnlp)
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| Challenge: | Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a document. |
| Approach: | They propose a document-level relation extraction framework that captures and exploits instructive information by adding extra syntactic information into text representations. |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
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| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
| Approach: | They propose a document-level relation extraction model that empowers relational reasoning across sentences by automatically inducing the latent document- level graph. |
| Outcome: | The proposed model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results. |
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction (2022.naacl-main)
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| Challenge: | Existing methods for relation extraction only implicitly learn to model relevant contexts and entity types while being trained for RE. |
| Approach: | They propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE. |
| Outcome: | The proposed method outperforms the runner-up method on three benchmarks by 5.04% . textual contexts and entity types are the major information sources that lead to the success of previous approaches. |
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning (2022.coling-1)
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| Challenge: | Existing document-level relation extraction methods are sparse in relational entity pairs and the representation of entity pairs is insufficient. |
| Approach: | They propose a Pair-Aware and Entity-Enhanced(PAEE) model to solve two challenges . they propose predicting potential relational entity pairs and assembling directional entity pairs . |
| Outcome: | The proposed model can obtain state-of-the-art performance on four benchmark datasets . it can predict potential relational entity pairs and assemble directional entity pairs . |
Re2-DocRED: Revisiting Revisited-DocRED for Joint Entity and Relation Extraction (2026.eacl-long)
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| Challenge: | Document-level Joint Entity and Relation Extraction benchmarks such as DocRED, Re-DocRED, and DocGNRE suffer from pervasive False Negatives (FN) |
| Approach: | They propose a training-free annotation pipeline that leverages user-specifiable reasoning, enriched inverse/co-occurring relation schemas, and novel entity-level constraints to address FN gaps. |
| Outcome: | The proposed pipeline improves on REDFM Mandarin dataset and shows that model recall scores drop on revised splits, whereas the training set mitigates this. |
Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction (2022.naacl-main)
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| Challenge: | Existing work on document-level relation extraction has focused on end-to-end setting that extracts global entities and relations jointly. |
| Approach: | They propose to introduce a two-way interaction between COREF and RE that is specifically designed to leverage task characteristics, bridging decisions of two tasks for direct task interference. |
| Outcome: | The proposed model achieves the best performance by up to 2.3/5.1 F1 over the baseline. |
Few-Shot Document-Level Relation Extraction (2022.naacl-main)
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| Challenge: | Existing benchmarks for relation extraction are built on sentence-level corpora, but document-level ones provide more realism. |
| Approach: | They propose a few-shot document-level relation extraction benchmark based on document-based corpora. |
| Outcome: | The proposed benchmark is based on two existing supervised learning data sets, DocRED and sciERC. |
Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED (2022.acl-long)
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| Challenge: | Document-level relation extraction is a challenging task as it requires reasoning across multiple sentences. |
| Approach: | They propose to use a recommend-revise scheme to reduce the workload of annotators by providing them with candidate relation instances from distant supervision to supplement and remove relational facts. |
| Outcome: | The proposed dataset is the first large-scale and human-annotated dataset for relation extraction. |
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)
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| Challenge: | Existing approaches to document-level relation extraction are difficult to establish direct connections between distant entity pairs. |
| Approach: | They propose a global context-enhanced Graph Convolutional Network model which captures rich global context information of entities in a document. |
| Outcome: | The proposed model captures rich global context information of entities in a document. |
Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction (2022.emnlp-main)
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| Challenge: | Using incomplete annotations, we find that false negative samples are prevalent in the DocRED dataset . we reannotate 4,053 documents in the dataset by adding the missed relation triples back to the original DocRED. |
| Approach: | They propose to re-annotate 4,053 documents in the document-level relation extraction dataset by adding missing relation triples back to the original DocRED. |
| Outcome: | The proposed dataset improves on the existing DocRED dataset by 13 F1 points. |
A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction (2023.acl-long)
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| Challenge: | Existing document-level relation extraction methods assume entities and their mentions are given beforehand, which is inadequate for real-world applications. |
| Approach: | They propose a table-to-graph generation model for joint extraction of entities and relations at document-level. |
| Outcome: | The proposed model surpasses existing methods by a large margin and achieves state-of-the-art results on a document-level relation extraction dataset. |
On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations (2024.findings-acl)
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| Challenge: | Existing DocRE models which perform well may make more mistakes when merely changing the entity names in the document, hindering the generalization to novel entity names. |
| Approach: | They propose a pipeline to generate entity-renamed documents by replacing the original entity names with names from Wikidata. |
| Outcome: | The proposed pipeline generates entity-renamed documents by replacing the original entity names with names from Wikidata. |