# FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding

arXiv: https://arxiv.org/abs/2009.08138 · HTML: https://mlsanity.com/paper/2009.08138/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [FLEX: Unifying Evaluation for Few-Shot NLP](https://mlsanity.com/paper/2107.07170/) (2021)
- [Controllable Semantic Parsing via Retrieval Augmentation](https://mlsanity.com/paper/2110.08458/) (2021)
- [Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind](https://mlsanity.com/paper/2211.04684/) (2022)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [Few-shot learning for medical text: A systematic review](https://mlsanity.com/paper/2204.14081/) (2022)
- [Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.07343/) (2021)
- [MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning](https://mlsanity.com/paper/2304.09402/) (2023)
- [Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2312.13495/) (2023)
- [Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF](https://mlsanity.com/paper/2112.04999/) (2021)
- [Problems and Countermeasures in Natural Language Processing Evaluation](https://mlsanity.com/paper/2104.09712/) (2021)

## Abstract (arXiv)

Few-shot learning (FSL) is one of the key future steps in machine learning and has raised a lot of attention. However, in contrast to the rapid development in other domains, such as Computer Vision, the progress of FSL in Nature Language Processing (NLP) is much slower. One of the key reasons for this is the lacking of public benchmarks. NLP FSL researches always report new results on their own constructed few-shot datasets, which is pretty inefficient in results comparison and thus impedes cumulative progress. In this paper, we present FewJoint, a novel Few-Shot Learning benchmark for NLP. Different from most NLP FSL research that only focus on simple N-classification problems, our benchmark introduces few-shot joint dialogue language understanding, which additionally covers the structure prediction and multi-task reliance problems. This allows our benchmark to reflect the real-word NLP complexity beyond simple N-classification. Our benchmark is used in the few-shot learning contest of SMP2020-ECDT task-1. We also provide a compatible FSL platform to ease experiment set-up.