Few-Shot Relation Classification
8 papers with code • 4 benchmarks • 6 datasets
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.
General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction.
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers.
This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification.
We present FewRel 2. 0, 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?