In this paper, we investigate few-shot joint learning for dialogue language
understanding. Most existing few-shot models learn a single task each time with
only a few examples. However, dialogue language understanding contains two
closely related tasks, i.e., intent detection and slot filling, and often
benefits from jointly learning the two tasks. This calls for new few-shot
learning techniques that are able to capture task relations from only a few
examples and jointly learn multiple tasks. To achieve this, we propose a
similarity-based few-shot learning scheme, named Contrastive Prototype Merging
network (ConProm), that learns to bridge metric spaces of intent and slot on
data-rich domains, and then adapt the bridged metric space to the specific
few-shot domain. Experiments on two public datasets, Snips and FewJoint, show
that our model significantly outperforms the strong baselines in one and five
shots settings.

本文研究了针对对话语言理解的 few-shot 联合学习，提出了一种基于相似度的 few-shot 学习方案 ConProm, 能够从少量样例捕捉任务关系并联合学习多个任务。在 Snips 和 FewJoint 两个公开数据集上的实验结果表明，该方案表现出比强基线模型更好的性能。

学习桥接度量空间：少样本联合学习意图检测和槽位填充

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent  Detection and Slot Filling

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.

本文提出 FewJoint，一个针对 NLP 的 Few-Shot Learning 基准测试，并在 SMP2020-ECDT 的 Few-Shot Learning 比赛中使用此基准测试。