Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) models achieve consistent performance gains in DocRE, but their underlying decision rules are still understudied. |
| Approach: | They propose to use annotations to provide rationales for document-level relation extraction (DocRE) they then propose to apply a method to evaluate models' reasoning capabilities . |
| Outcome: | The proposed models exhibit different reasoning processes in contrast to humans . the proposed models render models more trustworthy and robust to be deployed in real-world scenarios. |
Similar Papers
Rethinking Document-Level Relation Extraction: A Reality Check (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent efforts push up performance boundaries of document-level relation extraction (DocRE) but these efforts are not promising. |
| Approach: | They construct four types of entity mention attacks to examine model robustness . they also have a close check on model usability in a more realistic setting . |
| Outcome: | The proposed model is based on a strong or untenable assumption in common . the model is robust under four types of mention attacks and usable in a realistic setting . |
End-to-end Learning of Logical Rules for Enhancing Document-level Relation Extraction (2024.acl-long)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) aims to extract relations between entities in a document. |
| Approach: | They propose a rule-based framework that jointly learns DocRE and logical rules . they parameterize a Rule reasoning module to simulate the inference of logical rule . |
| Outcome: | The proposed framework improves DocRE models by a significant margin on four benchmark datasets. |
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling (2022.findings-emnlp)
Copied to clipboard
| 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. |
AutoRE: Document-Level Relation Extraction with Large Language Models (2024.acl-demos)
Copied to clipboard
| Challenge: | Existing methods for relation extraction are limited to Sentence-level Relation Extraction (SentRE) tasks. |
| Approach: | They propose an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. |
| Outcome: | The proposed model surpasses TAG by 10.03% and 9.03% on the dev and test set. |
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
Copied to clipboard
| 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. |
Document-Level Relation Extraction with Global Relations and Entity Pair Reasoning (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing document-level relation extraction models focus on individual entity pairs, limiting their ability to handle complex reasoning tasks. |
| Approach: | They propose a document-level relation extraction framework based on global relations and entity pair reasoning that captures fine-grained interactions between entity pairs. |
| Outcome: | The proposed framework outperforms existing models on widely-used datasets. |
Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for document-level relation extraction relied on implicitly powerful representations, which makes the model less transparent. |
| Approach: | They propose a probabilistic model for document-level relation extraction by learning logic rules. |
| Outcome: | The proposed model outperforms baseline models in relation performance and logical consistency. |
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing (2022.naacl-main)
Copied to clipboard
| Challenge: | Document-level relation extraction models are not robust and exhibit bizarre behaviors when non-evidence sentences are removed. |
| Approach: | They propose a document-level relation extraction framework that uses a sentence importance score and a focusing loss to encourage DocRE models to focus on evidence sentences. |
| Outcome: | The proposed framework improves overall performance and makes DocRE models more robust. |
Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer (2024.lrec-main)
Copied to clipboard
| Challenge: | Document-level Relation Extraction (DocRE) is the task of extracting all semantic relationships from a document. |
| Approach: | They propose to transfer an English document to Japanese to promote DocRE in other languages. |
| Outcome: | The proposed model reduces the human edit steps by 50% compared with the previous approach. |
Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction (2023.eacl-main)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) predicts relations for entity pairs relying on context-dependent reasoning . a large number of annotation errors can make it difficult to distinguish large semantically close relations . |
| Approach: | They propose a loss function to improve discriminability and robustness for DocRE . they also propose supervised contrastive learning and negative label sampling strategy . |
| Outcome: | The proposed method achieves state-of-the-art results on the DocRED dataset and its recently cleaned version. |