Challenge: Existing methods focus on normal class and fail to extract relational triplets precisely.
Approach: They propose an end-to-end model which can jointly extract relational triplets from sentences . they employ two different strategies in decoding process: employing only one united decoder or applying multiple separated decodeurs.
Outcome: The proposed model outperforms the baseline method significantly in two datasets.

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Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning (D19-1)

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Challenge: Existing works didn’t consider the extraction order of relational facts in a sentence.
Approach: They propose to take the extraction order into consideration by applying reinforcement learning into a sequence-to-sequence model.
Outcome: The proposed model could generate relational facts freely.
Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning (2021.findings-acl)

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Challenge: Existing models for relational fact extraction do not analyze the output data structure from the perspective of graph representation flexibility and heterogeneity.
Approach: They propose a relational fact extraction model based on graph-oriented analytical perspective that outperforms other models.
Outcome: The proposed model outperforms state-of-the-art models on two benchmark datasets and shows that it is flexible and space-efficient.
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction (2021.emnlp-main)

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Challenge: State-of-the-art NLP models adopt shallow heuristics that limit their generalization capability.
Approach: They propose to use heuristics that limit their generalization capability to model lexical overlap with the training set in Named-Entity Recognition and Event or Type heuristic in Relation Extraction to test their models.
Outcome: The proposed model can perform better on the two key tasks, while the retention of training relation triples.
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction (2024.lrec-main)

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Challenge: Existing large language models can extract triples from simple sentences with few-shot learning or fine-tuning, but they often miss out when extracting from complex sentences.
Approach: They propose an evaluation-filtering framework that integrates large language models with small models for relational triple extraction tasks.
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REBEL: Relation Extraction By End-to-end Language generation (2021.findings-emnlp)

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Challenge: Existing approaches to extract relation triplets from text often involve multiple-step pipelines that propagate errors or are limited to a small number of relation types.
Approach: They propose to use autoregressive seq2seq models to simplify Relation Extraction by expressing triplets as a sequence of text and a model that performs end-to-end relation extraction for more than 200 different relation types.
Outcome: The proposed model achieves state-of-the-art on an array of Relation Extraction and Relation Classification benchmarks and achieves top performance in most of them.
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.
Outcome: The proposed model achieves 4.6% and 0.9% improvement over current methods in the NYT24 and NYT29 benchmark datasets.
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.
Outcome: The proposed approach achieves an 8.16 speedup with a slight reduction in accuracy on standard benchmarks.
Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism (D19-66)

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Challenge: Existing studies on relation extrac-tion focus on finding only one relation between two entities in a single sentence.
Approach: They propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism that finds n-to-1 subject-object relations by using a forward decoder and a backward decode-r.
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Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning (D18-1)

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Challenge: Existing methods for extracting relations are slow and lack precision . a novel approach to extract relations is proposed to reduce noise between sentences .
Approach: They propose a word-level distant supervised approach for relation extraction using New York Times and Freebase.
Outcome: The proposed method improves the area of precision/call(PR) from 0.35 to 0.39 over the state-of-the-art methods.
Neural Relation Extraction for Knowledge Base Enrichment (P19-1)

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Challenge: Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment.
Approach: They propose an end-to-end relation extraction model for knowledge base enrichment based on a neural encoder-decoder model . they propose to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end to end manner.
Outcome: The proposed model outperforms state-of-the-art baselines by 15.51% and 8.38% on two real-world datasets.

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