47 citations · 58 across the 6 of their papers we have counts for
6 papers
Efficient Ternary Weight Embedding Model: Bridging Scalability and Performance
Jiayi Chen, Chen Wu, Shaoqun Zhang +3
Embedding models have become essential tools in both natural language processing and computer vision, enabling efficient semantic search, recommendation, clustering, and more. Howe…
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
Shitao Xiao, Zheng Liu, Weihao Han +9
Ad-hoc search calls for the selection of appropriate answers from a massive-scale corpus. Nowadays, the embedding-based retrieval (EBR) becomes a promising solution, where deep lea…
Improving Non-autoregressive Generation with Mixup Training
Ting Jiang, Shaohan Huang, Zihan Zhang +6
While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation t…
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search
Chaozhuo Li, Bochen Pang, Yuming Liu +7
Sponsored search ads appear next to search results when people look for products and services on search engines. In recent years, they have become one of the most lucrative channel…
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…
Learning Fast Matching Models from Weak Annotations
Xue Li, Zhipeng Luo, Hao Sun +5
This paper proposes a novel training scheme for fast matching models in Search Ads, which is motivated by the real challenges in model training. The first challenge stems from the…