# Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?

arXiv: https://arxiv.org/abs/2310.00696 · HTML: https://mlsanity.com/paper/2310.00696/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 51 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://mlsanity.com/paper/2005.06312/) (2020)
- [Double Graph Based Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2009.13752/) (2020)
- [Attention Guided Graph Convolutional Networks for Relation Extraction](https://mlsanity.com/paper/1906.07510/) (2019)
- [Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction](https://mlsanity.com/paper/1911.09886/) (2019)
- [TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://mlsanity.com/paper/2010.13415/) (2020)
- [Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction](https://mlsanity.com/paper/2102.10249/) (2021)
- [Distant Supervision for Relation Extraction beyond the Sentence Boundary](https://mlsanity.com/paper/1609.04873/) (2016)
- [Joint Entity and Relation Extraction with Set Prediction Networks](https://mlsanity.com/paper/2011.01675/) (2020)
- [Document-Level N-ary Relation Extraction with Multiscale Representation Learning](https://mlsanity.com/paper/1904.02347/) (2019)
- [A Hierarchical Framework for Relation Extraction with Reinforcement Learning](https://mlsanity.com/paper/1811.03925/) (2018)
- [Packed Levitated Marker for Entity and Relation Extraction](https://mlsanity.com/paper/2109.06067/) (2021)
- [Global-to-Local Neural Networks for Document-Level Relation Extraction](https://mlsanity.com/paper/2009.10359/) (2020)
- [Fine-tune Bert for DocRED with Two-step Process](https://mlsanity.com/paper/1909.11898/) (2019)
- [HIN: Hierarchical Inference Network for Document-Level Relation Extraction](https://mlsanity.com/paper/2003.12754/) (2020)
- [A Partition Filter Network for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2108.12202/) (2021)
- [Document-Level Relation Extraction with Reconstruction](https://mlsanity.com/paper/2012.11384/) (2020)
- [PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction](https://mlsanity.com/paper/2106.09895/) (2021)
- [Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation](https://mlsanity.com/paper/2203.10900/) (2022)
- [UniRE: A Unified Label Space for Entity Relation Extraction](https://mlsanity.com/paper/2107.04292/) (2021)
- [Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention](https://mlsanity.com/paper/1804.06987/) (2018)
- [SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2106.01709/) (2021)
- [Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion](https://mlsanity.com/paper/2106.08657/) (2021)
- [OneRel:Joint Entity and Relation Extraction with One Module in One Step](https://mlsanity.com/paper/2203.05412/) (2022)
- [N-ary Relation Extraction using Graph State LSTM](https://mlsanity.com/paper/1808.09101/) (2018)
- [Discriminative Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2106.01562/) (2021)
- [SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction](https://mlsanity.com/paper/2109.12093/) (2021)

## Abstract (arXiv)

Two distinct approaches have been proposed for relational triple extraction - pipeline and joint. Joint models, which capture interactions across triples, are the more recent development, and have been shown to outperform pipeline models for sentence-level extraction tasks. Document-level extraction is a more challenging setting where interactions across triples can be long-range, and individual triples can also span across sentences. Joint models have not been applied for document-level tasks so far. In this paper, we benchmark state-of-the-art pipeline and joint extraction models on sentence-level as well as document-level datasets. Our experiments show that while joint models outperform pipeline models significantly for sentence-level extraction, their performance drops sharply below that of pipeline models for the document-level dataset.