117 citations · 529 across the 70 of their papers we have counts for
8 papers · 1 filter
Pre-training Methods in Information Retrieval
Yixing Fan, Xiaohui Xie, Yinqiong Cai +5
The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need. In r…
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…
Are Neural Ranking Models Robust?
Chen Wu, Ruqing Zhang, Jiafeng Guo +2
Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field. So far, much effort has been devoted to developing effective neural ranking…
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…
B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrieval
Xinyu Ma, Jiafeng Guo, Ruqing Zhang +3
Pre-training and fine-tuning have achieved remarkable success in many downstream natural language processing (NLP) tasks. Recently, pre-training methods tailored for information re…
Semantic Models for the First-stage Retrieval: A Comprehensive Review
Jiafeng Guo, Yinqiong Cai, Yixing Fan +3
Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents, and latter stag…
A Linguistic Study on Relevance Modeling in Information Retrieval
Yixing Fan, Jiafeng Guo, Xinyu Ma +3
Relevance plays a central role in information retrieval (IR), which has received extensive studies starting from the 20th century. The definition and the modeling of relevance has…
Learning to Truncate Ranked Lists for Information Retrieval
Chen Wu, Ruqing Zhang, Jiafeng Guo +3
Ranked list truncation is of critical importance in a variety of professional information retrieval applications such as patent search or legal search. The goal is to dynamically d…