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Few-Shot Relation Classification is a particular relation classification task under minimum annotated data, where a model is required to classify a new incoming query instance given only few support instances (e.g., 1 or 5) during testing.
Source: MICK: A Meta-Learning Framework for Few-shot Relation Classification with Little Training Data
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The FewRel (Few-Shot Relation Classification Dataset) contains 100 relations and 70,000 instances from Wikipedia. The dataset is divided into three subsets: training set (64 relations), validation set (16 relations) and test set (20 relations). Source: Neural Snowball for Few-Shot Relation Learning Image Source: https://www.aclweb.org/anthology/D18-1514.pdf
DocRED (Document-Level Relation Extraction Dataset) is a relation extraction dataset constructed from Wikipedia and Wikidata. Each document in the dataset is human-annotated with named entity mentions, coreference information, intra- and inter-sentence relations, and supporting evidence. DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document. Along with the human-annotated data, the dataset provides large-scale distantly supervised data. DocRED contains 132,375 entities and 56,354 relational facts annotated on 5,053 Wikipedia documents. In addition to the human-annotated data, the dataset provides large-scale distantly supervised data over 101,873 documents. Source: DocRED: A Large-Scale Document-Level Relation Extraction Dataset Image Source: DocRED: A Large-Scale Document-Level Relation Extraction Dataset
SciERC dataset is a collection of 500 scientific abstract annotated with scientific entities, their relations, and coreference clusters. The abstracts are taken from 12 AI conference/workshop proceedings in four AI communities, from the Semantic Scholar Corpus. SciERC extends previous datasets in scientific articles SemEval 2017 Task 10 and SemEval 2018 Task 7 by extending entity types, relation types, relation coverage, and adding cross-sentence relations using coreference links. Source: http://nlp.cs.washington.edu/sciIE/ Image Source: http://nlp.cs.washington.edu/sciIE/
A more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? (2) Can they detect none-of-the-above (NOTA) relations? Source: FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
FREDo is a Few-Shot Document-Level Relation Extraction Benchmark based on DocRED and SciERC. The dataset is divided into four subsets: training set (62 relations), validation set (16 relations), in-domain test set (16 relations), and cross-domain test set (7 relations).
Green family of datasets for emergent communications on relations. By comparison with other relations datasets, TexRel provides rapid training and experimentation, whilst being sufficiently large to avoid overfitting in the context of emergent communications.
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