10 citations · 10 across the 4 of their papers we have counts for
4 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…
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation
Jian Guan, Zhuoer Feng, Yamei Chen +4
Standard multi-task benchmarks are essential for developing pretraining models that can generalize to various downstream tasks. Existing benchmarks for natural language processing…
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…