72 citations · 383 across the 38 of their papers we have counts for
6 papers · 2 filters
PARADE: Passage Representation Aggregation for Document Reranking
Canjia Li, Andrew Yates, Sean MacAvaney +2
Pretrained transformer models, such as BERT and T5, have shown to be highly effective at ad-hoc passage and document ranking. Due to inherent sequence length limits of these models…
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…