most citedFedMatch: Federated Learning Over Heterogeneous Question Answering Data

30 citations · 104 across the 20 of their papers we have counts for

collaborators

23 papers

cs.IR202110 cited

Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3

Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of stor…

cs.CL2021

Integrating Deep Event-Level and Script-Level Information for Script Event Prediction

Long Bai, Saiping Guan, Jiafeng Guo +3

Scripts are structured sequences of events together with the participants, which are extracted from the texts.Script event prediction aims to predict the subsequent event given the…

cs.IR202130 cited

FedMatch: Federated Learning Over Heterogeneous Question Answering Data

Jiangui Chen, Ruqing Zhang, Jiafeng Guo +2

Question Answering (QA), a popular and promising technique for intelligent information access, faces a dilemma about data as most other AI techniques. On one hand, modern QA method…

cs.IR20211 cited

Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance

Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3

Recently, Information Retrieval community has witnessed fast-paced advances in Dense Retrieval (DR), which performs first-stage retrieval with embedding-based search. Despite the i…

cs.IR20212 cited

Toward the Understanding of Deep Text Matching Models for Information Retrieval

Lijuan Chen, Yanyan Lan, Liang Pang +2

Semantic text matching is a critical problem in information retrieval. Recently, deep learning techniques have been widely used in this area and obtained significant performance im…

cs.IR20211 cited

A Discriminative Semantic Ranker for Question Retrieval

Yinqiong Cai, Yixing Fan, Jiafeng Guo +3

Similar question retrieval is a core task in community-based question answering (CQA) services. To balance the effectiveness and efficiency, the question retrieval system is typica…