84 citations · 308 across the 21 of their papers we have counts for
6 papers · 1 filter
Taming Sparsely Activated Transformer with Stochastic Experts
Simiao Zuo, Xiaodong Liu, Jian Jiao +5
Sparsely activated models (SAMs), such as Mixture-of-Experts (MoE), can easily scale to have outrageously large amounts of parameters without significant increase in computational…
KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning
Haonan Li, Yeyun Gong, Jian Jiao +3
Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural languag…
EL-Attention: Memory Efficient Lossless Attention for Generation
Yu Yan, Jiusheng Chen, Weizhen Qi +4
Transformer model with multi-head attention requires caching intermediate results for efficient inference in generation tasks. However, cache brings new memory-related costs and pr…
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
Weizhen Qi, Yeyun Gong, Yu Yan +9
Now, the pre-training technique is ubiquitous in natural language processing field. ProphetNet is a pre-training based natural language generation method which shows powerful perfo…
Mask Attention Networks: Rethinking and Strengthen Transformer
Zhihao Fan, Yeyun Gong, Dayiheng Liu +6
Transformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores t…
TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search
Jason Yue Zhu, Yanling Cui, Yuming Liu +7
Text encoders based on C-DSSM or transformers have demonstrated strong performance in many Natural Language Processing (NLP) tasks. Low latency variants of these models have also b…