241 citations · 802 across the 37 of their papers we have counts for
14 papers · 1 filter
DRAMA: Domain Retrieval using Adaptive Module Allocation
Pranav Kasela, Marco Braga, Ophir Frieder +3
Neural models are increasingly used in Web-scale Information Retrieval (IR). However, relying on these models introduces substantial computational and energy requirements, leading…
GRIT: Graph-based Recall Improvement for Task-oriented E-commerce Queries
Hrishikesh Kulkarni, Surya Kallumadi, Sean MacAvaney +2
Many e-commerce search pipelines have four stages, namely: retrieval, filtering, ranking, and personalized-reranking. The retrieval stage must be efficient and yield high recall be…
LexBoost: Improving Lexical Document Retrieval with Nearest Neighbors
Hrishikesh Kulkarni, Nazli Goharian, Ophir Frieder +1
Sparse retrieval methods like BM25 are based on lexical overlap, focusing on the surface form of the terms that appear in the query and the document. The use of inverted indices in…
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…
Simplified Data Wrangling with ir_datasets
Sean MacAvaney, Andrew Yates, Sergey Feldman +3
Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset documentation is scattered across the Internet and once one obtains a copy of the data, the…
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego +3
Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their computational expenses deem them…
Training Curricula for Open Domain Answer Re-Ranking
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego +3
In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking str…
Expansion via Prediction of Importance with Contextualization
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego +3
The identification of relevance with little textual context is a primary challenge in passage retrieval. We address this problem with a representation-based ranking approach that:…
SLEDGE: A Simple Yet Effective Baseline for COVID-19 Scientific Knowledge Search
Sean MacAvaney, Arman Cohan, Nazli Goharian
With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of literature on the virus. Clinicians, resear…
Ranking Significant Discrepancies in Clinical Reports
Sean MacAvaney, Arman Cohan, Nazli Goharian +1
Medical errors are a major public health concern and a leading cause of death worldwide. Many healthcare centers and hospitals use reporting systems where medical practitioners wri…
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning
Sean MacAvaney, Luca Soldaini, Nazli Goharian
While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages. This is prim…
CEDR: Contextualized Embeddings for Document Ranking
Sean MacAvaney, Andrew Yates, Arman Cohan +1
Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to thes…
Overcoming low-utility facets for complex answer retrieval
Sean MacAvaney, Andrew Yates, Arman Cohan +4
Many questions cannot be answered simply; their answers must include numerous nuanced details and additional context. Complex Answer Retrieval (CAR) is the retrieval of answers to…
Characterizing Question Facets for Complex Answer Retrieval
Sean MacAvaney, Andrew Yates, Arman Cohan +4
Complex answer retrieval (CAR) is the process of retrieving answers to questions that have multifaceted or nuanced answers. In this work, we present two novel approaches for CAR ba…