Therefore, we focus on the true few-shot paradigm targeting to construct a novel training set that accommodates the low-resource scenarios. In particular, we propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection (FSED), consisting of the following two ...
此外,在Experiments部分作者对Few Shot Event Detection和Supervised Event Detection进行了实验,在Analysis and Discussion部分作者讨论了Automatic VS Human Verbalizers和Impact of NULL Instances以及Model Design Choices的问题,在此不作赘述,可以在原文详细查看。 6 Conclusions and Future Work 在本文中,作者提出了 P4...
我们通过以下几种方式来适应基于度量的few-shot learning方法:(1)对其进行修改以适合开放式声音事件检测问题公式;(2)提出一种无需任何额外人工即可自动构建标记的negative样本的方法 ,(3)提出一种推理时间inference time数据强化方法以提高检测准确性,(4)对语音的few-shot关键词检测任务进行广泛的评估,以及(5)验证了...
Most supervised systems of event detection (ED) task reply heavily on manual annotations and suffer from high-cost human effort when applied to new event types. To tackle this general problem, we turn our attention to few-shot learning (FSL). As a typical solution to FSL, cross-modal ...
Few-shot Bioacoustic Event Detection with Machine Learning Methods 基于机器学习方法的少发生物声事件检测 Few-shot learning is a type of classification through which predictions are made based on a limited number of samples for each class. This type of classification is sometimes referred to as a ...
A metric-learning method for few-shot cross-event rumor detection Neurocomputing (2023) C. Castillo, M. Mendoza, B. Poblete, Information credibility on twitter, in: Proceedings of the 20th International... S. Feng, R. Banerjee, Y. Choi, Syntactic stylometry for deception detection, in: Proc...
Label hallucination for few-shot classification STUNT: Few-shot tabular learning with self-generated tasks from unlabeled tables Unsupervised meta-learning via few-shot pseudo-supervised contrastive learning Progressive mix-up for few-shot supervised multi-source domain transfer ...
Few-NERD:一个Few-shot场景的命名实体识别数据集 0% 展开列表 近来,围绕着 "少样本命名实体识别"(few-shot NER)这一主题,出现了大量的工作和文献。“少样本命名实体识别”任务具有实际应用价值,也充满挑战性。但是目前鲜有专门针对该任务的基准数据,之前的大多数研究都是通过重新组织现有的有监督NER数据集,使其成...
Shot Learning problem. We propose a Dynamic-Memory-Based Prototypical Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn better prototypes for event types, but also produce more robust sentence encodings for event mentions. Differing from vanilla prototypical networks ...
which focuses on learning to detect a new event class with limited data, while retaining the ability to detect old classes to the extent possible. We created a benchmark dataset IFSED for the few-shot incremental event detection task based on FewEvent and propose two benchmarks, IFSED-K an...