Paper Info
Title
A Cooperative Neural Information Retrieval Pipeline with Knowledge Enhanced Automatic Query Reformulation
Abstract
ABSTRACTThis paper presents a neural information retrieval pipeline that integrates cooperative learning of query reformulation and neural retrieval models. Our pipeline first exploits an automatic query reformulator to reformulate the user-issued query and then submits the reformulated query to the neural retrieval model. We simultaneously optimize the quality of reformulated queries and ranking performance with an alternate training strategy where query reformulator and neural retrieval model learn from the feedback of each other. Besides, we incorporate knowledge information into automatic query reformulation. The reformulated queries are further improved and contribute to a better ranking performance of the following neural retrieval model. We study two representative neural retrieval models KNRM and BERT in our pipeline. Experiments on two datasets show that our pipeline consistently improves the retrieval performance of the original neural retrieval models while only increases negligible time on automatic query reformulation.
Year
DOI
Venue
2022
10.1145/3488560.3498516
WSDM
Keywords
DocType
Citations 
Neural IR, Query reformulation, Knowledge graph
Conference
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Xiangsheng Li1253.84
Jiaxin Mao216426.30
Weiwei Guo38816.18
Zhijing Wu4254.15
Yiqun Liu51592136.51
Min Zhang6118.01
Shaoping Ma71544126.00
Zhaowei Wang851.76
Xiuqiang He900.34