13 citations · 23 across the 5 of their papers we have counts for
5 papers
GUIM -- General User and Item Embedding with Mixture of Representation in E-commerce
Chao Yang, Ru He, Fangquan Lin +3
Our goal is to build general representation (embedding) for each user and each product item across Alibaba's businesses, including Taobao and Tmall which are among the world's bigg…
Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives
Wei Wang, Liangzhu Ge, Jingqiao Zhang +1
Following SimCSE, contrastive learning based methods have achieved the state-of-the-art (SOTA) performance in learning sentence embeddings. However, the unsupervised contrastive le…
SAS: Self-Augmentation Strategy for Language Model Pre-training
Yifei Xu, Jingqiao Zhang, Ru He +4
The core of self-supervised learning for pre-training language models includes pre-training task design as well as appropriate data augmentation. Most data augmentations in languag…
Progressively Stacking 2.0: A Multi-stage Layerwise Training Method for BERT Training Speedup
Cheng Yang, Shengnan Wang, Chao Yang +3
Pre-trained language models, such as BERT, have achieved significant accuracy gain in many natural language processing tasks. Despite its effectiveness, the huge number of paramete…
CoRe: An Efficient Coarse-refined Training Framework for BERT
Cheng Yang, Shengnan Wang, Yuechuan Li +4
In recent years, BERT has made significant breakthroughs on many natural language processing tasks and attracted great attentions. Despite its accuracy gains, the BERT model genera…