1 code implementation • 20 Mar 2024 • Pranav Kasela, Gabriella Pasi, Raffaele Perego, Nicola Tonellotto
Open-domain question answering requires retrieval systems able to cope with the diverse and varied nature of questions, providing accurate answers across a broad spectrum of query types and topics.
1 code implementation • 20 Sep 2023 • Pranav Kasela, Gabriella Pasi, Raffaele Perego
The problem of personalization in Information Retrieval has been under study for a long time.
1 code implementation • 28 Jun 2023 • Pranav Kasela, Marco Braga, Gabriella Pasi, Raffaele Perego
Personalization in Information Retrieval is a topic studied for a long time.
no code implementations • 21 Jun 2023 • Vincenzo Paparella, Vito Walter Anelli, Franco Maria Nardini, Raffaele Perego, Tommaso Di Noia
To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier.
no code implementations • 25 Nov 2022 • Ophir Frieder, Ida Mele, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto
Our achieved high cache hit rates significantly improve the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end.
1 code implementation • 1 Jun 2022 • Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Alberto Veneri
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models.
1 code implementation • 6 May 2021 • Francesco Busolin, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani
Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees.
no code implementations • 17 Nov 2020 • Lorenzo Valerio, Franco Maria Nardini, Andrea Passarella, Raffaele Perego
Results show that DynHP compresses a NN up to $10$ times without significant performance drops (up to $3. 5\%$ additional error w. r. t.
no code implementations • 30 Apr 2020 • Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani
In this paper, we investigate the novel problem of \textit{query-level early exiting}, aimed at deciding the profitability of early stopping the traversal of the ranking ensemble for all the candidate documents to be scored for a query, by simply returning a ranking based on the additive scores computed by a limited portion of the ensemble.
1 code implementation • 29 Apr 2020 • Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder
We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process.
1 code implementation • 29 Apr 2020 • Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder
Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking.
1 code implementation • 29 Apr 2020 • Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder
We also observe that the performance is additive with the current leading first-stage retrieval methods, further narrowing the gap between inexpensive and cost-prohibitive passage ranking approaches.
no code implementations • 9 Jan 2020 • Ida Mele, Nicola Tonellotto, Ophir Frieder, Raffaele Perego
The results of queries characterized by a topic are kept in the fraction of the cache dedicated to it.
1 code implementation • 16 Apr 2019 • Raffaele Perego, Giulio Ermanno Pibiri, Rossano Venturini
The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations.