Papers Few-Shot Text Classification
“Few-Shot Text Classification” 태그가 달린 논문 106편 · 필터 해제
ASCM: An Answer Space Clustered Prompting Method without Answer Engineering
Prompt-based learning, which exploits knowledge from pre-trained language models by providing textual prompts and designing appropriate answer-category mapping methods, has achieved impressive successes on few-shot text …
Few-Shot Text ClassificationNatural Language Inferencetext-classificationText ClassificationEICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization
While the prompt-based fine-tuning methods had advanced few-shot natural language understanding tasks, self-training methods are also being explored. This work revisits the consistency regularization in self-training and…
Few-Shot LearningFew-Shot Text ClassificationLanguage ModelingLanguage Modelling+4Label Semantic Aware Pre-training for Few-shot Text Classification
In text classification tasks, useful information is encoded in the label names. Label semantic aware systems have leveraged this information for improved text classification performance during fine-tuning and prediction.…
ClassificationFew-Shot Text ClassificationSentencetext-classification+2Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model. In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective …
Few-Shot Text ClassificationLanguage ModelingLanguage Modellingtext-classification+1MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification
Text classification struggles to generalize to unseen classes with very few labeled text instances per class. In such a few-shot learning (FSL) setting, metric-based meta-learning approaches have shown promising results.…
Few-Shot LearningFew-Shot Text ClassificationMeta-Learningtext-classification+1Few-Shot Learning with Siamese Networks and Label Tuning
We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification. In recent years, an approach based on neural textual entailment models has been …
Few-Shot LearningFew-Shot Text ClassificationNatural Language Inferencetext-classification+1LST: Lexicon-Guided Self-Training for Few-Shot Text Classification
Self-training provides an effective means of using an extremely small amount of labeled data to create pseudo-labels for unlabeled data. Many state-of-the-art self-training approaches hinge on different regularization me…
ClassificationFew-Shot Text Classificationtext-classificationText ClassificationGrad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
Large pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks. However, fine tuning such models requires a large number of training examples for each…
Few-Shot Text ClassificationMeta-Learningtext-classificationText Classification+1Label-guided Data Augmentation for Prompt-based Few Shot Learners
Recent advances on large pre-trained language models (PLMs) lead impressive gains on many natural language understanding (NLU) tasks with task-specific fine-tuning. However, direct fine-tuning PLMs heavily rely on large …
Data AugmentationFew-Shot LearningFew-Shot Text ClassificationNatural Language Understanding+2ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification
Data augmentation has been an important ingredient for boosting performances of learned models. Prior data augmentation methods for few-shot text classification have led to great performance boosts. However, they have no…
ClassificationData AugmentationFew-Shot Text ClassificationSemi-Supervised Text Classification+3Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification
Data augmentation techniques are widely used for enhancing the performance of machine learning models by tackling class imbalance issues and data sparsity. State-of-the-art generative language models have been shown to p…
Active LearningClassificationData AugmentationFew-Shot Text Classification+2A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification
Due to the lack of labeled data in many realistic scenarios, a number of few-shot learning methods for text classification have been proposed, among which the meta learning based ones have recently attracted much attenti…
Few-Shot LearningFew-Shot Text ClassificationMeta-Learningtext-classification+1Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER
Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design t…
Domain AdaptationFew-Shot Text ClassificationIn-Context Learningnamed-entity-recognition+6Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification
Tuning pre-trained language models (PLMs) with task-specific prompts has been a promising approach for text classification. Particularly, previous studies suggest that prompt-tuning has remarkable superiority in the low-…
Few-Shot Text ClassificationLanguage ModelingLanguage ModellingMasked Language Modeling+2TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification
Recent studies have shown that prompts improve the performance of large pre-trained language models for few-shot text classification. Yet, it is unclear how the prompting knowledge can be transferred across similar NLP t…
Few-Shot LearningFew-Shot Text Classificationtext-classificationText ClassificationGood Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER
Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design t…
Domain AdaptationFew-Shot Text ClassificationIn-Context Learningnamed-entity-recognition+6Few-Shot Learning with Siamese Networks and Label Tuning
We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification. In recent years, an approach based on neural textual entailment models has been …
Few-Shot LearningFew-Shot Text ClassificationNatural Language Inferencetext-classification+1RAFT: A Real-World Few-Shot Text Classification Benchmark
Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved …
ClassificationFew-Shot LearningFew-Shot Text Classificationtext-classification+1ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection
The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications. For example, intent classification in dialogue systems. The reason is that the inclusion of unsupporte…
Few-Shot Text Classificationintent-classificationIntent ClassificationOut of Distribution (OOD) Detection+3Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model. In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective …
ClassificationFew-Shot Text ClassificationLanguage ModelingLanguage Modelling+2