MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data

Geng, Xin; Chen, Xiwen; Zhu, Kenny Q.; Shen, Libin; Zhao, Yinggong

doi:10.1145/3340531.3411858

Cited by 18 publications

(15 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similarly, Meta-SGD [13] trained a meta-learner that can produce learner's initialization in just one step, on both supervised learning and reinforcement learning. To improve the performance of the model with less training data, MICK [14] aggregated cross-domain knowledge into models by open-source task enrichment. The model aimed to classify query instances, and sought basic knowledge about supporting examples to obtain a better example representation.…”

Section: Few-shot Relation Classificationmentioning

confidence: 99%

Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification

Jin

Zhang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Compared with the traditional few-shot task, the few-shot none-of-the-above (NOTA) relation classification focuses on the realistic scenario of few-shot learning, in which a test instance might not belong to any of the target categories. This undoubtedly increases the task’s difficulty because given only a few support samples, this cannot represent the distribution of NOTA categories in space. The model needs to make full use of the syntactic information and word meaning information learned in the pre-training stage to distinguish the NOTA category and the support sample category in the embedding space. However, previous fine-tuning methods mainly focus on optimizing the extra classifiers (on top of pre-trained language models (PLMs)) and neglect the connection between pre-training objectives and downstream tasks. In this paper, we propose the commonsense knowledge-aware prompt tuning (CKPT) method for a few-shot NOTA relation classification task. First, a simple and effective prompt-learning method is developed by constructing relation-oriented templates, which can further stimulate the rich knowledge distributed in PLMs to better serve downstream tasks. Second, external knowledge is incorporated into the model by a label-extension operation, which forms knowledgeable prompt tuning to improve and stabilize prompt tuning. Third, to distinguish the NOTA pairs and positive pairs in embedding space more accurately, a learned scoring strategy is proposed, which introduces a learned threshold classification function and improves the loss function by adding a new term focused on NOTA identification. Experiments on two widely used benchmarks (FewRel 2.0 and Few-shot TACRED) show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field.

show abstract

Section: Few-shot Relation Classificationmentioning

confidence: 99%

Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification

Jin

Zhang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Li et al [257] deviate by jointly training a prototype and instance encoder, enhancing the relation classification accuracy of the model. In addition, Mick [283], HiRe [258] and Meta-iKG [259] explore different facets: Mick explores FSRC from the perspective of limiting available data during training, HiRe focuses on hierarchical relational learning across different levels, while Meta-iKG utilizes local subgraphs for efficient pattern learning and generalization across few-shot and large-shot relations.…”

Section: Few-shot Relation Classificationmentioning

confidence: 99%

The danger of dichloromethane in the synthesis and application of imidazole-based dyes

Liu

Chen

et al. 2022

Dyes and Pigments

View full text Add to dashboard Cite

“…At present, Meta-learning is widely active in image classification [16,[28][29], image recognition [17,30], object detection [18,[31][32], and text classification [19] and other computer vision and natural language processing fields. And it has gradually drawn much attention in speech recognition [20], audio event recognition [21,33], text-to-speech [22], speaker recognition [23] and other areas of speech signal processing.…”

Section: A Related Work In Meta-learningmentioning

confidence: 99%

Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement

Zhou

2021

IEEE Access

View full text Add to dashboard Cite

Separating target speech from noisy signal is important for many realistic applications. Recently, deep neural network (DNN) has been widely used in speech enhancement (SE) and obtained prominent performance improvements. However, the current deep models require a large amount of training data to obtain a good performance. It is still challenging to construct an effective deep speech enhancement model with actual few training samples. At present, meta-learning has become the research focus of few-shot learning due to its capability of quickly process new tasks with few samples by the prior meta-knowledge, but there are very few works applying meta-learning on few-shot speech enhancement. In this paper, we propose a generic meta-learning framework Meta-SE which applies the U-Net as the metalearner, to tackle the few-shot speech enhancement problem. Meta-SE is trained and optimized with the changed speech enhancement tasks to obtain meta-knowledge, and towards better capability of fast and good generalizing to the new unseen noises with few training samples. The experiment results show that the proposed method not only outperforms the state-of-the-arts DNN-SE models under the few-shot conditions, but also learns a more general and flexible model for task adaption. INDEX TERMS Speech enhancement, single-channel, meta-learning, few-shot learning This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

show abstract

MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data

Cited by 18 publications

References 12 publications

Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification

Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification

The danger of dichloromethane in the synthesis and application of imidazole-based dyes

Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement

Contact Info

Product

Resources

About