108 citations · 444 across the 24 of their papers we have counts for
6 papers · 1 filter
Declarative Experimentation in Information Retrieval using PyTerrier
Craig Macdonald, Nicola Tonellotto
The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed in expressive high-level languages such as Python, have allowed more expressive representati…
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:…
Topic Propagation in Conversational Search
I. Mele, C. I. Muntean, F. M. Nardini +3
In a conversational context, a user expresses her multi-faceted information need as a sequence of natural-language questions, i.e., utterances. Starting from a given topic, the con…
Topical Result Caching in Web Search Engines
Ida Mele, Nicola Tonellotto, Ophir Frieder +1
Caching search results is employed in information retrieval systems to expedite query processing and reduce back-end server workload. Motivated by the observation that queries belo…