| Challenge: | Existing approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. |
| Approach: | They propose a method to extract multiple relations from a paragraph by encoding the paragraph only once. |
| Outcome: | The proposed approach can perform state-of-the-art on the benchmark ACE 2005. |
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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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A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)
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| Challenge: | Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction. |
| Approach: | They propose a pipelined approach for entity and relation extraction that uses two independent encoders to construct the relation model. |
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EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022.naacl-main)
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| Challenge: | Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction. |
| Approach: | They propose to explicitly introduce relation representation and jointly represent it with entities to identify valid triples. |
| Outcome: | The proposed method is based on ablations and document-level relation extraction and joint entity and relation extraction. |
ITER: Iterative Transformer-based Entity Recognition and Relation Extraction (2024.findings-emnlp)
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| Challenge: | Recent advances in NLP generate structured information in an autoregressive manner, causing low throughput . authors propose an efficient encoder-based relation extraction model that performs the task in three parallelizable steps. |
| Approach: | They propose an efficient encoder-based relation extraction model that performs the task in three parallelizable steps. |
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GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction (2021.findings-emnlp)
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| Challenge: | Existing models for extracting relation triplets suffer from incompletion and disorder problems when they extract multi-token entities from input sentences. |
| Approach: | They propose a special entity labelling method that fine-tunes the pre-trained model and learns the special entity labels simultaneously. |
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Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction (2021.findings-acl)
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| Challenge: | Existing models based on textual data do not capture context beyond the sentence. |
| Approach: | They propose a framework that enables the model to learn multi-omnics biological information about entities (proteins) with the help of additional multi-modal cues like molecular structure. |
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Pretrained Knowledge Base Embeddings for improved Sentential Relation Extraction (2022.acl-srw)
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| Challenge: | Existing models that perform explicit on-task training of graph embeddings are inadequate. |
| Approach: | They propose to combine pretrained knowledge base graph embeddings with transformer based language models to improve performance on sentential Relation Extraction task. |
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ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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| Challenge: | Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain. |
| Approach: | They propose a pre-training method to improve the joint extraction performance with just extra entity annotations. |
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Paragraph-based Transformer Pre-training for Multi-Sentence Inference (2022.naacl-main)
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| Challenge: | Recent studies show that pre-trained transformers perform poorly for multi-candidate inference tasks. |
| Approach: | They propose a pre-training objective that models paragraph-level semantics across multiple input sentences. |
| Outcome: | The proposed model outperforms existing models on three AS2 and one fact verification datasets. |
Pre-training Entity Relation Encoder with Intra-span and Inter-span Information (2020.emnlp-main)
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| Challenge: | Existing pre-trained models do not handle text spans and relation among text span pairs. |
| Approach: | They propose to integrate span-related information into pre-trained encoder for entity relation extraction task. |
| Outcome: | The proposed pre-training method outperforms distantly supervised pre-trained models on two entity relation extraction benchmark datasets. |