What Do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification (2022.acl-srw)
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| Challenge: | Existing RE surveys focus on modeling techniques, but there are few that are based on real-world scenarios. |
| Approach: | They propose to survey RE datasets and revisit the task definition and its adoption by the community. |
| Outcome: | The proposed approach improves the reliability of RE evaluations across multiple datasets and reveals significant discrepancies in annotations. |
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The State of Relation Extraction Data Quality: Is Bigger Always Better? (2024.findings-acl)
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| Challenge: | Relation extraction (RE) methods extract tuples of relationships from text . many datasets with frequent label errors have been used . |
| Approach: | They review recent surveys and a sample of recent RE methods papers . they find that real-time evaluations of RE methods are possible . |
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CrossRE: A Cross-Domain Dataset for Relation Extraction (2022.findings-emnlp)
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| Challenge: | Relation Extraction (RE) evaluation is limited to in-domain setups . despite the drought of research on cross-domain RE, its practical importance remains . |
| Approach: | They propose a cross-domain benchmark for relation extraction which includes multi-label annotations and meta-data to include explanations and flags of difficult instances. |
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More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)
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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
| Challenge: | Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically . |
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Relating Relations: Meta-Relation Extraction from Online Health Forum Posts (2021.eacl-srw)
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| Challenge: | Relation extraction is a key task in knowledge extraction, and is often defined as identifying relations that hold between entities in text. |
| Approach: | They propose to conceptualise relation extraction tasks for user-generated health texts and create a dataset and model for meta-relation extraction. |
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Relation Extraction using Explicit Context Conditioning (N19-1)
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| Challenge: | Existing methods for relation extraction fail to capture complex and long dependencies . end-to-end models that learn both NER and RE can solve this problem . |
| Approach: | They propose to use second-order relations to compute relation scores for relation extraction (RE) . they propose to combine second- and first-order relation scores to obtain final relation scores . |
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Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
| Approach: | They propose a model which combines [MASK] embeddings with entity embedds to learn relation embeddations. |
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Global Relation Embedding for Relation Extraction (N18-1)
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| Challenge: | Existing methods to extract textual relations with distant supervision are limited by their reliance on supervised training data. |
| Approach: | They propose to embed relations with global statistics of relations to combat the wrong labeling problem of distant supervision. |
| Outcome: | The proposed method is more robust to training noise introduced by distant supervision and improves relation extraction models. |
On the Role of Discriminative Models in Generative Relation Extraction (2026.acl-long)
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| Challenge: | Existing methods for relation extraction (RE) are discriminative and generative . previous studies show that discriminative models can support generative RE . |
| Approach: | They propose a framework that leverages discriminative models to produce a top-k set of candidate relations and integrates this knowledge into generative models via in-context or prompt learning. |
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Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction (2025.emnlp-main)
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| Challenge: | Existing approaches to multimodal relation extraction ignore structural constraints and lack semantic expressiveness for fine-grained relation understanding. |
| Approach: | They propose a framework that reformulates multimodal relation extraction as a retrieval task driven by relation semantics. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the benchmark datasets MNRE and MORE and exhibits stronger robustness and interpretability. |
SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents (2024.emnlp-main)
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| Challenge: | Scientific information extraction (SciIE) is critical for converting unstructured knowledge from scholarly articles into structured data. |
| Approach: | They propose to use a scientific entity and relation extraction dataset to capture interactions between entities in full texts. |
| Outcome: | The proposed dataset captures the intricate use and interactions among entities in full texts and provides an out-of-distribution test set to offer a more realistic evaluation. |