Search Results for author:
Found 6 papers, 4 papers with code
AdsCVLR: Commercial Visual-Linguistic Representation Modeling in Sponsored Search
Sponsored search advertisements (ads) appear next to search results when consumers look for products and services on search engines.
Ranked #3 on
Image-text matching
on CommercialAdsDataset
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification.
Improving Non-autoregressive Generation with Mixup Training
While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation tasks remains a challenge.
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search
Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling.
TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search
Text encoders based on C-DSSM or transformers have demonstrated strong performance in many Natural Language Processing (NLP) tasks.
Learning Fast Matching Models from Weak Annotations
The proposed training framework targets on mitigating both issues, by treating the stronger but undeployable models as annotators, and learning a deployable model from both human provided relevance labels and weakly annotated search log data.
