Search Results for author:
Found 11 papers, 6 papers with code
META: Metadata-Empowered Weak Supervision for Text Classification
Based on seed words, we rank and filter motif instances to distill highly label-indicative ones as {``}seed motifs{''}, which provide additional weak supervision.
Intermediate Training on Question Answering Datasets Improves Generative Data Augmentation
We improve generative data augmentation by formulating the data generation as context generation task and use question answering (QA) datasets for intermediate training.
LOPS: Learning Order Inspired Pseudo-Label Selection for Weakly Supervised Text Classification
Weakly supervised text classification methods typically train a deep neural classifier based on pseudo-labels.
Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data
Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases.
BFClass: A Backdoor-free Text Classification Framework
To recognize the poisoned subset, we examine the training samples with these identified triggers as the most suspicious token, and check if removing the trigger will change the poisoned model's prediction.
News Meets Microblog: Hashtag Annotation via Retriever-Generator
Hashtag annotation for microblog posts has been recently formulated as a sequence generation problem to handle emerging hashtags that are unseen in the training set.
X-Class: Text Classification with Extremely Weak Supervision
Finally, we pick the most confident documents from each cluster to train a text classifier.
Contextualized Weak Supervision for Text Classification
Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers.
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss
We extend the consistency of hierarchical classification algorithm over asymmetric tree distance loss.
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation.
User Bias Removal in Review Score Prediction
Review score prediction of text reviews has recently gained a lot of attention in recommendation systems.
