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Found 5 papers, 3 papers with code
Reproducibility Analysis and Enhancements for Multi-Aspect Dense Retriever with Aspect Learning
MADRAL was evaluated on proprietary data and its code was not released, making it challenging to validate its effectiveness on other datasets.
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval
Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e. g., products with aspects such as category and brand.
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval
Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings.
Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search
Then we fine-tune several pre-trained models and train an ensemble model to aggregate all the predictions from various pre-trained models with human-annotation data in the fine-tuning stage.
Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank
Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker.
