activity
20182020
most citedExpansion via Prediction of Importance with Contextualization

72 citations · 144 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CL2020

GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection

Sajad Sotudeh, Tong Xiang, Hao-Ren Yao +4

Offensive language detection is an important and challenging task in natural language processing. We present our submissions to the OffensEval 2020 shared task, which includes thre…

cs.IR202059 cited

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…

cs.IR20209 cited

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…

cs.IR202072 cited

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:…

cs.CL20203 cited

Interaction Matching for Long-Tail Multi-Label Classification

Sean MacAvaney, Franck Dernoncourt, Walter Chang +2

We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be u…

cs.IR20201 cited

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…