241 citations · 884 across the 50 of their papers we have counts for
11 papers · 2 filters
On the Effects of Regional Spelling Conventions in Retrieval Models
Andreas Chari, Sean MacAvaney, Iadh Ounis
One advantage of neural ranking models is that they are meant to generalise well in situations of synonymity i.e. where two words have similar or identical meanings. In this paper,…
Generative Query Reformulation for Effective Adhoc Search
Xiao Wang, Sean MacAvaney, Craig Macdonald +1
Performing automatic reformulations of a user's query is a popular paradigm used in information retrieval (IR) for improving effectiveness -- as exemplified by the pseudo-relevance…
Lexically-Accelerated Dense Retrieval
Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian +1
Retrieval approaches that score documents based on learned dense vectors (i.e., dense retrieval) rather than lexical signals (i.e., conventional retrieval) are increasingly popular…
Online Distillation for Pseudo-Relevance Feedback
Sean MacAvaney, Xi Wang
Model distillation has emerged as a prominent technique to improve neural search models. To date, distillation taken an offline approach, wherein a new neural model is trained to p…
Adaptive Latent Entity Expansion for Document Retrieval
Iain Mackie, Shubham Chatterjee, Sean MacAvaney +1
Despite considerable progress in neural relevance ranking techniques, search engines still struggle to process complex queries effectively - both in terms of precision and recall.…
The Information Retrieval Experiment Platform
Maik Fröbe, Jan Heinrich Reimer, Sean MacAvaney +6
We integrate ir_datasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and ev…
Adapting Learned Sparse Retrieval for Long Documents
Thong Nguyen, Sean MacAvaney, Andrew Yates
Learned sparse retrieval (LSR) is a family of neural retrieval methods that transform queries and documents into sparse weight vectors aligned with a vocabulary. While LSR approach…
Overview of the TREC 2022 NeuCLIR Track
Dawn Lawrie, Sean MacAvaney, James Mayfield +4
This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval. The main task in this y…
A Unified Framework for Learned Sparse Retrieval
Thong Nguyen, Sean MacAvaney, Andrew Yates
Learned sparse retrieval (LSR) is a family of first-stage retrieval methods that are trained to generate sparse lexical representations of queries and documents for use with an inv…
Doc2Query--: When Less is More
Mitko Gospodinov, Sean MacAvaney, Craig Macdonald
Doc2Query -- the process of expanding the content of a document before indexing using a sequence-to-sequence model -- has emerged as a prominent technique for improving the first-s…
One-Shot Labeling for Automatic Relevance Estimation
Sean MacAvaney, Luca Soldaini
Dealing with unjudged documents ("holes") in relevance assessments is a perennial problem when evaluating search systems with offline experiments. Holes can reduce the apparent eff…