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Found 5 papers, 2 papers with code
Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism
In this paper, we propose a new ranking model DR-BERT, which improves the Document Retrieval (DR) task by a task-adaptive training process and a Segmented Token Recovery Mechanism (STRM).
TITA: A Two-stage Interaction and Topic-Aware Text Matching Model
To tackle this problem, we define a three-level relevance in keyword-document matching task: topic-aware relevance, partially-relevance and irrelevance.
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.
FRI-Miner: Fuzzy Rare Itemset Mining
In some cases, infrequent or rare itemsets and rare association rules also play an important role in real-life applications.
Databases
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.
