5 citations · 7 across the 4 of their papers we have counts for
4 papers
EASE: Entity-Aware Contrastive Learning of Sentence Embedding
Sosuke Nishikawa, Ryokan Ri, Ikuya Yamada +2
We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities. The advantage of using entity supervision is…
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently
Futa Waseda, Sosuke Nishikawa, Trung-Nghia Le +2
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 p…
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification
Sosuke Nishikawa, Ikuya Yamada, Yoshimasa Tsuruoka +1
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 langu…
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings
Sosuke Nishikawa, Ryokan Ri, Yoshimasa Tsuruoka
Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingua…