Few-Shot Relation Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 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.

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

6 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (23 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.

• 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.
• Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
  25 Mar 2024 1 repository listed
  In this paper, we introduce a novel approach to enhance information extraction combining multiple sentence representations and contrastive learning.
• CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
  18 Oct 2023 1 repository listed Syntology ran 3 of 7 samples · 4 unverified · 7 pointer-only (licence)
  To evaluate the performance of state-of-the-art RC models on the CORE dataset, we conduct experiments in the few-shot domain adaptation setting.
• Dependency-aware Prototype Learning for Few-shot Relation Classification
  1 Oct 2022 1 repository listed
  This paper presents a novel dependency-aware prototype learning (DAPL) method for few-shot relation classification.
• Few-Shot Document-Level Relation Extraction
  4 May 2022 1 repository listed
  We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark.
• Towards Realistic Few-Shot Relation Extraction
  1 Nov 2021 1 repository listed
  In recent years, few-shot models have been applied successfully to a variety of NLP tasks.
• Meta-Information Guided Meta-Learning for Few-Shot Relation Classification
  1 Dec 2020 1 repository listed
  Few-shot classification requires classifiers to adapt to new classes with only a few training instances.
• FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
  16 Oct 2019 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  We present FewRel 2.
• Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
  16 Jun 2019 1 repository listed
  This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification.
• FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
  24 Oct 2018 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers.

Syntology lines on 4 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.