100 citations · 183 across the 23 of their papers we have counts for
5 papers · 2 filters
Enhancing Dense Retrievers' Robustness with Group-level Reweighting
Peixuan Han, Zhenghao Liu, Zhiyuan Liu +1
The anchor-document data derived from web graphs offers a wealth of paired information for training dense retrieval models in an unsupervised manner. However, unsupervised data con…
MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin
Tianshuo Zhou, Sen Mei, Xinze Li +5
This paper proposes Multi-modAl Retrieval model via Visual modulE pLugin (MARVEL), which learns an embedding space for queries and multi-modal documents to conduct retrieval. MARVE…
Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
Zhenghao Liu, Sen Mei, Chenyan Xiong +5
This paper proposes Text mAtching based SequenTial rEcommendation model (TASTE), which maps items and users in an embedding space and recommends items by matching their text repres…
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
Xinze Li, Zhenghao Liu, Chenyan Xiong +4
This paper presents Structure Aware Dense Retrieval (SANTA) model, which encodes user queries and structured data in one universal embedding space for retrieving structured data. S…
Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
Shi Yu, Chenghao Fan, Chenyan Xiong +3
Common document ranking pipelines in search systems are cascade systems that involve multiple ranking layers to integrate different information step-by-step. In this paper, we prop…