Relation Extraction

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

51 leaderboard tables shown for this task, 50 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 51 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

82 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 82 until expanded.

Subtasks archive 2025-07-28

15 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 735 papers with code (1,977 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• LayoutLM: Pre-training of Text and Layout for Document Image Understanding
  31 Dec 2019 19 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 1 pointer-only (licence)
  In this paper, we propose the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image…
• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
  25 Jan 2019 19 repositories listed Syntology ran 4 of 25 samples · 21 unverified · 1 pointer-only (licence)
  Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
• Matching the Blanks: Distributional Similarity for Relation Learning
  7 Jun 2019 13 repositories listed Syntology ran 2 of 14 samples · 12 unverified · 1 pointer-only (licence)
  General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction.
• Are Transformers Effective for Time Series Forecasting?
  26 May 2022 10 repositories listed Syntology ran 13 of 19 samples · 6 unverified · 13 pointer-only (licence)
  Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task.
• LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
  29 Dec 2020 9 repositories listed
  Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born…
• LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
  2 Oct 2020 9 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
• Simplifying Graph Convolutional Networks
  19 Feb 2019 7 repositories listed Syntology ran 3 of 8 samples · 5 unverified
  Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
• SpanBERT: Improving Pre-training by Representing and Predicting Spans
  24 Jul 2019 6 repositories listed Syntology ran 3 of 15 samples · 12 unverified · 4 pointer-only (licence)
  We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text.
• Enriching Pre-trained Language Model with Entity Information for Relation Classification
  20 May 2019 6 repositories listed Syntology ran 3 of 8 samples · 5 unverified
  In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task.
• SciBERT: A Pretrained Language Model for Scientific Text
  26 Mar 2019 6 repositories listed
  Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
• The Natural Language Decathlon: Multitask Learning as Question Answering
  20 Jun 2018 6 repositories listed Syntology ran 1 of 13 samples · 12 unverified · 4 pointer-only (licence)
  Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
• Joint entity recognition and relation extraction as a multi-head selection problem
  20 Apr 2018 6 repositories listed
  State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers.
• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
  29 Oct 2020 5 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining.
• Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
  16 Mar 2020 5 repositories listed Syntology ran 16 of 30 samples · 14 unverified · 28 pointer-only (licence)
  We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.
• A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
  7 Sep 2019 5 repositories listed
  Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction.
• Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention
  19 Apr 2018 5 repositories listed
  Relation extraction is the problem of classifying the relationship between two entities in a given sentence.
• BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
  19 Oct 2022 4 repositories listed
  Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain.
• LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
  18 Apr 2022 4 repositories listed
  In this paper, we propose \textbf{LayoutLMv3} to pre-train multimodal Transformers for Document AI with unified text and image masking.
• Locating and Editing Factual Associations in GPT
  10 Feb 2022 4 repositories listed Syntology ran 4 of 15 samples · 11 unverified
  To test our hypothesis that these computations correspond to factual association recall, we modify feed-forward weights to update specific factual associations using Rank-One Model Editing (ROME).
• KLUE: Korean Language Understanding Evaluation
  20 May 2021 4 repositories listed
  We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
• Entity, Relation, and Event Extraction with Contextualized Span Representations
  8 Sep 2019 4 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.
• DocRED: A Large-Scale Document-Level Relation Extraction Dataset
  14 Jun 2019 4 repositories listed
  Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for…
• Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets
  13 Jun 2019 4 repositories listed Syntology ran 0 of 2 samples · 2 unverified
• Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction
  25 May 2022 3 repositories listed
  We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset.
• Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
  20 Feb 2021 3 repositories listed
  Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN.
• Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!
  22 Sep 2020 3 repositories listed
  Despite efforts to distinguish three different evaluation setups (Bekoulis et al., 2018), numerous end-to-end Relation Extraction (RE) articles present unreliable performance comparison to previous work.
• Dialogue-Based Relation Extraction
  17 Apr 2020 3 repositories listed Syntology ran 9 of 9 samples · 0 unverified · 5 pointer-only (licence)
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue.
• Generalizing Natural Language Analysis through Span-relation Representations
  10 Nov 2019 3 repositories listed
  Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures.
• Span-based Joint Entity and Relation Extraction with Transformer Pre-training
  17 Sep 2019 3 repositories listed
  The model is trained using strong within-sentence negative samples, which are efficiently extracted in a single BERT pass.
• Simple BERT Models for Relation Extraction and Semantic Role Labeling
  10 Apr 2019 3 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  We present simple BERT-based models for relation extraction and semantic role labeling.

Syntology lines on 16 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.