198 citations · 949 across the 29 of their papers we have counts for
10 papers · 1 filter
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning
Shicheng Xu, Liang Pang, Huawei Shen +1
Text matching is a fundamental technique in both information retrieval and natural language processing. Text matching tasks share the same paradigm that determines the relationship…
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…
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm
Lin Bo, Liang Pang, Gang Wang +3
Recently, pre-trained language models such as BERT have been applied to document ranking for information retrieval, which first pre-train a general language model on an unlabeled l…
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
Liang Pang, Jun Xu, Qingyao Ai +3
In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal…
A Deep Look into Neural Ranking Models for Information Retrieval
Jiafeng Guo, Yixing Fan, Liang Pang +6
Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from tr…
Learning Visual Features from Snapshots for Web Search
Yixing Fan, Jiafeng Guo, Yanyan Lan +3
When applying learning to rank algorithms to Web search, a large number of features are usually designed to capture the relevance signals. Most of these features are computed based…
DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval
Liang Pang, Yanyan Lan, Jiafeng Guo +3
This paper concerns a deep learning approach to relevance ranking in information retrieval (IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to ge…
A Deep Investigation of Deep IR Models
Liang Pang, Yanyan Lan, Jiafeng Guo +2
The effective of information retrieval (IR) systems have become more important than ever. Deep IR models have gained increasing attention for its ability to automatically learning…
MatchZoo: A Toolkit for Deep Text Matching
Yixing Fan, Liang Pang, JianPeng Hou +3
In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared…
A Study of MatchPyramid Models on Ad-hoc Retrieval
Liang Pang, Yanyan Lan, Jiafeng Guo +2
Deep neural networks have been successfully applied to many text matching tasks, such as paraphrase identification, question answering, and machine translation. Although ad-hoc ret…