42 citations · 107 across the 4 of their papers we have counts for
6 papers
A Replication Study of Dense Passage Retriever
Xueguang Ma, Kai Sun, Ronak Pradeep +1
Text retrieval using learned dense representations has recently emerged as a promising alternative to "traditional" text retrieval using sparse bag-of-words representations. One re…
Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations
Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin +3
Pyserini is an easy-to-use Python toolkit that supports replicable IR research by providing effective first-stage retrieval in a multi-stage ranking architecture. Our toolkit is se…
The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models
Ronak Pradeep, Rodrigo Nogueira, Jimmy Lin
We propose a design pattern for tackling text ranking problems, dubbed "Expando-Mono-Duo", that has been empirically validated for a number of ad hoc retrieval tasks in different d…
Scientific Claim Verification with VERT5ERINI
Ronak Pradeep, Xueguang Ma, Rodrigo Nogueira +1
This work describes the adaptation of a pretrained sequence-to-sequence model to the task of scientific claim verification in the biomedical domain. We propose VERT5ERINI that expl…
Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset
Edwin Zhang, Nikhil Gupta, Raphael Tang +8
We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institut…
Document Ranking with a Pretrained Sequence-to-Sequence Model
Rodrigo Nogueira, Zhiying Jiang, Jimmy Lin
This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the task of document ranking. Our approach is fundamentally different from a commonly-adopted cl…