Search Results for author: Nicola Tonellotto

Found 14 papers, 11 papers with code

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

Adaptive Re-Ranking with a Corpus Graph

1 code implementation18 Aug 2022 Sean MacAvaney, Nicola Tonellotto, Craig Macdonald

Search systems often employ a re-ranking pipeline, wherein documents (or passages) from an initial pool of candidates are assigned new ranking scores.

Passage Ranking Re-Ranking +1

Lecture Notes on Neural Information Retrieval

no code implementations27 Jul 2022 Nicola Tonellotto

These lecture notes focus on the recent advancements in neural information retrieval, with particular emphasis on the systems and models exploiting transformer networks.

Information Retrieval Retrieval

Faster Learned Sparse Retrieval with Guided Traversal

1 code implementation24 Apr 2022 Antonio Mallia, Joel Mackenzie, Torsten Suel, Nicola Tonellotto

Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25.

Information Retrieval Retrieval

On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval

1 code implementation25 Aug 2021 Craig Macdonald, Nicola Tonellotto

In this work, we investigate the use of ANN scores for ranking the candidate documents, in order to decrease the number of candidate documents being fully scored.

Passage Ranking Retrieval

Query Embedding Pruning for Dense Retrieval

1 code implementation23 Aug 2021 Nicola Tonellotto, Craig Macdonald

Recent advances in dense retrieval techniques have offered the promise of being able not just to re-rank documents using contextualised language models such as BERT, but also to use such models to identify documents from the collection in the first place.

Passage Ranking Retrieval

On Single and Multiple Representations in Dense Passage Retrieval

1 code implementation13 Aug 2021 Craig Macdonald, Nicola Tonellotto, Iadh Ounis

The advent of contextualised language models has brought gains in search effectiveness, not just when applied for re-ranking the output of classical weighting models such as BM25, but also when used directly for passage indexing and retrieval, a technique which is called dense retrieval.

Passage Retrieval Re-Ranking +1

Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval

3 code implementations21 Jun 2021 Xiao Wang, Craig Macdonald, Nicola Tonellotto, Iadh Ounis

In particular, based on the pseudo-relevant set of documents identified using a first-pass dense retrieval, we extract representative feedback embeddings (using KMeans clustering) -- while ensuring that these embeddings discriminate among passages (based on IDF) -- which are then added to the query representation.

Information Retrieval Passage Ranking +2

Learning Passage Impacts for Inverted Indexes

1 code implementation24 Apr 2021 Antonio Mallia, Omar Khattab, Nicola Tonellotto, Torsten Suel

Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-rank this set using contextualized language models such as BERT.

Information Retrieval Language Modelling +2

Declarative Experimentation in Information Retrieval using PyTerrier

8 code implementations28 Jul 2020 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 representations of deep neural network architectures.

Information Retrieval Retrieval

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

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.


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

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