Papers Zero-Shot Text Classification
“Zero-Shot Text Classification” 태그가 달린 논문 48편 · 필터 해제
IBM MNLP IE at CASE 2021 Task 2: NLI Reranking for Zero-Shot Text Classification
Supervised models can achieve very high accuracy for fine-grained text classification. In practice, however, training data may be abundant for some types but scarce or even non-existent for others. We propose a hybrid ar…
ClassificationNatural Language InferenceRerankingTask 2+3Making Pre-trained Language Models Better Few-shot Learners
The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few…
Few-Shot LearningZero-Shot Text ClassificationUnsupervised Label Refinement Improves Dataless Text Classification
Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising, it crucially relies on accurate descri…
ClassificationClusteringGeneral ClassificationText Classification+1Text Classification Using Label Names Only: A Language Model Self-Training Approach
Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Humans can perform classification without…
ClassificationDocument ClassificationGeneral ClassificationLanguage Modeling+7Zero-shot Text Classification via Reinforced Self-training
Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity. In this situation, transferring from seen classes to unseen c…
ClassificationGeneral Classificationreinforcement-learningReinforcement Learning (RL)+4Zero-shot Text Classification With Generative Language Models
This work investigates the use of natural language to enable zero-shot model adaptation to new tasks. We use text and metadata from social commenting platforms as a source for a simple pretraining task. We then provide t…
ClassificationGeneral ClassificationLanguage ModelingLanguage Modelling+3Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
Zero-shot text classification (0Shot-TC) is a challenging NLU problem to which little attention has been paid by the research community. 0Shot-TC aims to associate an appropriate label with a piece of text, irrespective …
ArticlesBenchmarkingClassificationGeneral Classification+3Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the le…
ClassificationData AugmentationGeneral ClassificationGeneral Knowledge+4