Papers Few-Shot Text Classification
“Few-Shot Text Classification” 태그가 달린 논문 106편 · 필터 해제
Few-shot Text Classification with Distributional Signatures
In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly …
ClassificationFew-Shot Text ClassificationGeneral ClassificationMeta-Learning+3Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure t…
ClassificationFew-Shot Text ClassificationGeneral ClassificationInductive Bias+8Induction Networks for Few-Shot Text Classification
Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new …
Few-Shot Text ClassificationGeneral Classificationintent-classificationIntent Classification+4On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure t…
ClassificationFew-Shot Text ClassificationGeneral ClassificationInductive Bias+7Diverse Few-Shot Text Classification with Multiple Metrics
We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low inter-task variance, we consider a more …
ClassificationFew-Shot LearningFew-Shot Text ClassificationGeneral Classification+7Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop
Most of the literature around text classification treats it as a supervised learning problem: given a corpus of labeled documents, train a classifier such that it can accurately predict the classes of unseen documents. I…
Few-Shot LearningFew-Shot Text ClassificationGeneral ClassificationManagement+3