Papers Zero-Shot Text Classification
“Zero-Shot Text Classification” 태그가 달린 논문 48편 · 필터 해제
Evaluating TCFD Reporting: A New Application of Zero-Shot Analysis to Climate-Related Financial Disclosures
We examine climate-related disclosures in a large sample of reports published by banks that officially endorsed the recommendations of the Task Force for Climate-related Financial Disclosures (TCFD). In doing so, we intr…
text-classificationText ClassificationZero-Shot Text ClassificationEmpowering Sentence Encoders with Prompting and Label Retrieval for Zero-shot Text Classification
With contrastive pre-training, sentence encoders are generally optimized to locate semantically similar samples closer to each other in their embedding spaces. In this work, we focus on the potential of their embedding s…
ClassificationDescriptiveMultiple-choicePseudo Label+7Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches
Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches attempt to classify instances based on …
Classificationtext-classificationText ClassificationUnsupervised Text Classification+1Zero-Shot Text Classification with Self-Training
Recent advances in large pretrained language models have increased attention to zero-shot text classification. In particular, models finetuned on natural language inference datasets have been widely adopted as zero-shot …
ClassificationNatural Language Inferencetext-classificationText Classification+1Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations
Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners. However, most existing zero-shot methods involve heavy human engineering or complicated self-training pipelines, hindering thei…
ClusteringSentenceSentence EmbeddingsSentiment Analysis+5Don't Prompt, Search! Mining-based Zero-Shot Learning with Language Models
Masked language models like BERT can perform text classification in a zero-shot fashion by reformulating downstream tasks as text infilling. However, this approach is highly sensitive to the template used to prompt the m…
Text ClassificationText InfillingZero-Shot LearningZero-Shot Text ClassificationConformal Predictor for Improving Zero-shot Text Classification Efficiency
Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification. 0shot models based on natural language inference (NLI) and next sentence prediction (NSP) employ cross-encoder archi…
ClassificationNatural Language InferenceSentencetext-classification+3Hypothesis Engineering for Zero-Shot Hate Speech Detection
Standard approaches to hate speech detection rely on sufficient available hate speech annotations. Extending previous work that repurposes natural language inference (NLI) models for zero-shot text classification, we pro…
Hate Speech DetectionNatural Language InferenceText ClassificationZero-Shot Text ClassificationWhat Makes Pre-trained Language Models Better Zero-shot Learners?
Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori. This is not ideal because in a r…
Language ModellingPrompt Learningtext-classificationText Classification+1Template-based Approach to Zero-shot Intent Recognition
The recent advances in transfer learning techniques and pre-training of large contextualized encoders foster innovation in real-life applications, including dialog assistants. Practical needs of intent recognition requir…
Intent RecognitionNatural Language InferenceSentenceSentence Pair Modeling+4Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Prompt learning approaches have made waves in natural language processing by inducing better few-shot performance while they still follow a parametric-based learning paradigm; the oblivion and rote memorization problems …
Few-Shot Text ClassificationMemorizationNamed Entity Recognition (NER)Prompt Engineering+4Learn to Adapt for Generalized Zero-Shot Text Classification
Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned paramete…
ClassificationGeneralized Zero-Shot LearningMeta-Learningtext-classification+3Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification
Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set. Most existing LMTC approaches rely on massive human-annotated training data, which are …
Contrastive LearningMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+2Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
Pretrained language models (PLMs) have demonstrated remarkable performance in various natural language processing tasks: Unidirectional PLMs (e.g., GPT) are well known for their superior text generation capabilities; bid…
Few-Shot LearningMNLI-mNatural Language UnderstandingSST-2+3ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling
Traditional text classification approaches often require a good amount of labeled data, which is difficult to obtain, especially in restricted domains or less widespread languages. This lack of labeled data has led to th…
Classificationtext-classificationText ClassificationXLM-R+2Learn to Adapt for Generalized Zero-Shot Text Classification
Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned paramete…
ClassificationGeneralized Zero-Shot LearningMeta-Learningtext-classification+3NSP-BERT: A Prompt-based Few-Shot Learner Through an Original Pre-training Task--Next Sentence Prediction
Using prompts to utilize language models to perform various downstream tasks, also known as prompt-based learning or prompt-learning, has lately gained significant success in comparison to the pre-train and fine-tune par…
Entity LinkingLanguage ModelingLanguage ModellingPrompt Learning+3Efficient Multilingual Text Classification for Indian Languages
India is one of the richest language hubs on the earth and is very diverse and multilingual. But apart from a few Indian languages, most of them are still considered to be resource poor. Since most of the NLP techniques …
ClassificationMultilingual text classificationtext-classificationText Classification+1Issues with Entailment-based Zero-shot Text Classification
The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be ent…
ClassificationNatural Language InferenceSentencetext-classification+2Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification
A common issue in real-world applications of named entity recognition and classification (NERC) is the absence of annotated data for the target entity classes during training. Zero-shot learning approaches address this i…
Machine Reading Comprehensionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+6