Search Results for author: Raffaele Perego

Found 14 papers, 9 papers with code

DESIRE-ME: Domain-Enhanced Supervised Information REtrieval using Mixture-of-Experts

1 code implementation20 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.

Information Retrieval Open-Domain Question Answering +1

SE-PEF: a Resource for Personalized Expert Finding

1 code implementation20 Sep 2023 Pranav Kasela, Gabriella Pasi, Raffaele Perego

The problem of personalization in Information Retrieval has been under study for a long time.

Information Retrieval Retrieval

Caching Historical Embeddings in Conversational Search

no code implementations25 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.

Conversational Search Document Embedding +1

ILMART: Interpretable Ranking with Constrained LambdaMART

1 code implementation1 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.

Learning-To-Rank

Learning Early Exit Strategies for Additive Ranking Ensembles

1 code implementation6 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.

Dynamic Hard Pruning of Neural Networks at the Edge of the Internet

no code implementations17 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.

Edge-computing

Query-level Early Exit for Additive Learning-to-Rank Ensembles

no code implementations30 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.

Learning-To-Rank

Training Curricula for Open Domain Answer Re-Ranking

1 code implementation29 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.

Re-Ranking

Expansion via Prediction of Importance with Contextualization

1 code implementation29 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.

Language Modelling Passage Ranking +2

Topical Result Caching in Web Search Engines

no code implementations9 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.

Information Retrieval Retrieval

Compressed Indexes for Fast Search of Semantic Data

1 code implementation16 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.

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