activity
20182026
most citedExpansion via Prediction of Importance with Contextualization

72 citations · 348 across the 35 of their papers we have counts for

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Showing 2020Show all

8 papers · 1 filter

cs.CL2020

SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature 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 scientific literature on the virus. Clinici…

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.IR2020★ 59 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.IR2020★ 9 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.IR2020★ 72 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.CL2020★ 3 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…