5 papers
Reproducibility Analysis and Enhancements for Multi-Aspect Dense Retriever with Aspect Learning
Keping Bi, Xiaojie Sun, Jiafeng Guo +1
Multi-aspect dense retrieval aims to incorporate aspect information (e.g., brand and category) into dual encoders to facilitate relevance matching. As an early and representative m…
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval
Xiaojie Sun, Keping Bi, Jiafeng Guo +5
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…
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval
Xiaojie Sun, Keping Bi, Jiafeng Guo +5
Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with mult…
Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search
Xiaojie Sun, Lulu Yu, Yiting Wang +2
An effective ranking model usually requires a large amount of training data to learn the relevance between documents and queries. User clicks are often used as training data since…
Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank
Lulu Yu, Yiting Wang, Xiaojie Sun +2
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. In th…