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Found 4 papers, 1 papers with code
EASE: Entity-Aware Contrastive Learning of Sentence Embedding
We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities.
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently
Deep neural networks are vulnerable to adversarial examples (AEs), which have adversarial transferability: AEs generated for the source model can mislead another (target) model's predictions.
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification
We present a multilingual bag-of-entities model that effectively boosts the performance of zero-shot cross-lingual text classification by extending a multilingual pre-trained language model (e. g., M-BERT).
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings
Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora.
