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Found 8 papers, 5 papers with code
Weakly Supervised Anomaly Detection: A Survey
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation
We introduce GENIUS: a conditional text generation model using sketches as input, which can fill in the missing contexts for a given sketch (key information consisting of textual spans, phrases, or words, concatenated by mask tokens).
IDEA: Interactive DoublE Attentions from Label Embedding for Text Classification
Current text classification methods typically encode the text merely into embedding before a naive or complicated classifier, which ignores the suggestive information contained in the label text.
Selective Text Augmentation with Word Roles for Low-Resource Text Classification
Different words may play different roles in text classification, which inspires us to strategically select the proper roles for text augmentation.
ADBench: Anomaly Detection Benchmark
Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data?
American Hate Crime Trends Prediction with Event Extraction
Social media platforms may provide potential space for discourses that contain hate speech, and even worse, can act as a propagation mechanism for hate crimes.
Neural News Recommendation with Event Extraction
These methods encode news content on the word level and jointly train the attention parameters in the recommendation network, leading to more corpora being required to train the model.
Label Confusion Learning to Enhance Text Classification Models
LCM can learn label confusion to capture semantic overlap among labels by calculating the similarity between instances and labels during training and generate a better label distribution to replace the original one-hot label vector, thus improving the final classification performance.
